movie review research paper

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Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

Sambandam jayalakshmi, narayanan ganesh, janakiraman senthil murugan.

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Correspondence: [email protected]

Received 2022 May 13; Accepted 2022 Jun 27; Collection date 2022 Jul.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K -means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

Keywords: movie recommender, filtering techniques, performance metrics, K -means, metaheuristics

1. Introduction

Modern technology has revolutionized the volume, variety, and velocity at which data are generated. Digitalization of day-to-day experiences has led to the big data era. However, the enormous data have also led to the problem of information overload. Information overload may be defined as the state of being overwhelmed by the sheer volume of data presented to an average human for processing and decision making. Data mining methods can aid in obtaining and processing the relevant data and deal with the issue of information overload. Perhaps the most widely exploited tool among data mining methods is recommender systems.

Recommender systems work by assessing the available information about the likely patterns of the users and making suggestions from the information available [ 1 ]. The suggestions from the recommender systems help the system users find what is most suitable for them. Recommender systems are designed to ease product or service searches based on the least information available about the features [ 2 ]. A combination of various factors is used to assess the correlations in patterns and user characteristics to determine the best product suggestions for the customers [ 3 ].

The development of recommender systems depends on the field of application. The major application is in e-commerce websites where they suggest to the users the products or services based on the information available such as past search, age, gender, and other preferences [ 4 ]. They are also applied in job search platforms where the website suggests to a candidate the best possible positions fit for the skills. Since various industries have moved from an age of little available data to the era of big data, the junk information available is so much that it can delay the decision-making process. The recommender systems are typically made to ease the information search over the online systems so that the users find a more convenient way to connect to their preferences [ 5 ].

One of the applications of recommender systems is suggesting movies to watch to customers based on their preferences data. Movie recommender systems work by assessing the characteristic features of the users to make endorsements to the customers on what is best suited for them. It works by assessing the age, the previous preferences, gender, the content, context, and other demographic data to propose the movies. It checks the similarity among the users and items in the system to determine what could best fit the new user [ 6 ]. For example, a child will most likely receive recommendations for movies that children watch such as cartoons and animations based on the best similarity index for the children. Apart from that, children of various ages have different types of cartoons/animations to watch, and the recommender systems will propose the best depending on what other children of the same age are watching.

Movie recommender systems have helped the users overcome the chunk of information online to find only what is suited for them [ 7 ]. They use data mining techniques that match the similarities and help users find what is best suited for them [ 8 ]. Various criteria determine how the recommender systems work. The criteria are based on machine learning or deep learning algorithms that are used in matching the similarities before the suggestions are made. The algorithms achieve different levels of accuracy and require different computational times to retrieve the suggestions. Various computational algorithms have been proposed and used to increase the efficiency of recommender systems e [ 9 ]. However, each algorithm has its advantages and disadvantages; these make using the systems meet various needs based on their strengths. To reduce the limitations of each, the algorithms may be combined so that they perform better in making the recommendations [ 10 ].

This review paper aims to assess the challenges of recommender systems and make propositions to increase the accuracy of the systems. It assesses the recommendation approaches, the evaluation criteria of their efficiency, the challenges of these approaches, and possible solutions. A systematic literature review is conducted to determine the findings of the operational characteristics of the various recommendation approaches used and the performance criteria. The author aims to suggest the best solutions to make the approaches work better to achieve the operational expectations of the users.

The rest of the paper is arranged as follows: Section 2 details the methodology followed in this article. Section 3 describes different types of recommendation systems. Section 4 highlights some of the most popular machine learning algorithms used in movie recommender systems. Section 5 details the commonly used metaheuristic algorithms in movie recommendation tasks. Model metrics used for verifying the accuracy of recommendation systems are discussed in Section 6 . Some common problems with recommendation systems are discussed in detail in Section 7 . A critical discussion is presented in Section 8 . Section 9 presents the concluding remarks and the limitations of the study.

2. Review Methodology

This section describes the method used in obtaining information for the literature review. Peer-reviewed sources were used to gather information about movie recommender systems. The databases used were EBSCO Academic Search Premier, ScienceDirect, IEEE Library, ResearchGate, SpringerLink, and the ACM Portal. Google Scholar was also used to find leads to specific aspects of recommender systems for review.

Search Descriptors: Some of the keywords used in finding information about the movie recommender systems were “movie recommender systems”, “movie personalization”, “algorithms used in movie recommender systems”, “filtering techniques in movie recommender systems”, and “machine learning model metrics and measurement criteria”.

Inclusion Criteria: The inclusion criteria were papers that had information about recommender systems, the information had to be from published peer-reviewed sources. The paper abstracts were read to verify the validity of their information for use in this study. The exclusion criteria were papers that had grey literature on recommendation systems. The inclusion criteria for the articles and the methodology steps are summarized in Table 1 and Figure 1 .

Selection criteria for including sources in this review.

Steps in conducting the systematic review.

3. Movie Recommendation Systems

Movie recommendation work by filtering out data that is irrelevant and including only that which have matching characteristics or features [ 11 ]. As highlighted earlier, the world has moved from an era of scarcity of data online to an exponential growth in data. The systems work by manipulating the data to make sure it is efficient to drive data-driven decisions. In the jungle of available information about products, the systems need to evaluate what fits a certain customer and what does not. The systems go further in target and retargeting marketing to increase product viewership and hence increase the chance of the customers purchasing [ 12 ].

It is important for the developers to come up with systems that have higher performance characteristics and efficiency in matching the similarities in customer wants to seal the product sales or movie viewership [ 7 ]. The major types of filtering methods are collaborative filtering, content-based filtering, context-based filtering, and hybrid filtering.

3.1. Collaborative Filtering

Collaborative filtering works by matching the similarities in items and users. It looks at the characteristics of the users and the characteristics of the items the users have watched or searched for before [ 13 ]. In general, latent features obtained from rating matrices are looked at. In movie recommender systems, the recommendations are made based on the user information and what other people with similar user information are watching. For example, collaborative filtering in movie recommender systems picks the user demographic characteristics such as age, gender, and ethnicity [ 14 ]. Through these features, movie recommendations are made that match other people with similar demographic characteristics and previous user search history. Collaborative filtering suffers from a cold start if the user has not input any information, or the information is too little for any accurate clustering. In these cases, it does not know what to suggest [ 15 ]. The accuracy of the suggestion is also limited because people with similar demographic characteristics may not have similar preferences [ 16 ].

3.2. Content-Based Filtering

In contrast to collaborative filtering, content-based techniques employ user and item feature vectors to make recommendations. The fundamental differences between the two approaches are that content-based systems recommend items based on content features (no need for data about other users; recommendations about niche items, etc.) whereas collaborative filtering is based on user behaviour only and recommends items based on users with similar patterns (no domain knowledge; serendipity, etc.). A content-based filtering method works by making movie proposals to the user based on the content in the movies. It recognizes that clustering in the collaborative filtering recommendations may not match the preferences of the users [ 7 ]. The tastes and preferences of people with similar demographic characteristics are very different; what person X likes may not be similar to what person Y likes to watch. To solve this problem, content-based filtering algorithms give recommendations based on the contents of the movies [ 17 ]. In movie recommendations, some of the contents are the key characters and the genre of the movie.

3.3. Context-Based Filtering

This filtering technology is an improvement of the collaborative filtering method. It assumes that if person A and person B hold the same opinion on issue X, it is most likely that the same people will hold the same opinion/preference/thinking on a different issue Z. For example, if both people are attracted to Christmas movies from Netflix, it is most likely that they will still like Christmas movies by Showmax. The context-based filtering method recommends items with similar features or characteristics because the applications have just been extended to a different context [ 12 ]. It makes the same suggestions though the contexts are different. In most cases, web browsers import bookmarks and other settings when one upgrades from one browser to the next. This represents a change in context, since most of the settings and other items are imported into the new context, and the data available are used in making useful suggestions. Similarly, movie recommender systems may make a similar recommendation based on data from the previous context [ 18 ]. It is worth mentioning here about context-aware recommender systems (CARS), where the concept of context is well defined [ 19 , 20 , 21 , 22 ]. CARS acclimatize to the exact condition in which the recommended item will be used [ 23 , 24 , 25 , 26 ]. In this respect, CARS could avoid recommending a very long film to a user after a stressful day at work or suggest a romantic film if he/she is in the company of his/her partner.

3.4. Hybrid Filtering

This is a filtering technique that applies the concepts of all the other algorithms. It combines both collaborative filtering, content-based filtering, and context-based filtering to overcome the challenges of each method [ 10 ]. It is superior because it achieves higher performance in making the suggestions and also a faster computational time [ 11 ]. For instance, collaborative filtering may lack information about domain dependencies while content-based filtering lacks information about the preferences of the people [ 6 , 9 , 27 ]. A combination of these overcomes these challenges since user behaviour data and the content data are used to come up with recommendations.

4. Machine Leaning Algorithms for Movie Recommendation Systems

These are the algorithms that are used in filtering information and data mining so that the desired outcomes can be achieved. It is essential to understand the working of the information filtering methods so that the right algorithm is selected for the specific task in recommender systems [ 28 ].

4.1. K-Means Clustering

This is one of the simplest collaborative filtering approaches that categorizes the users based on their interests [ 29 ]. It is common for someone who wants to purchase an item to ask someone who has already purchased the product for their opinion. There is a higher chance that the influence of the current owner will affect the preferences and the tastes of the potentially new owner. Similarly, the algorithm compares the interesting features that can be associated with individuals that are classified to be within a group [ 30 ].

K -means clustering uses interests that are common among the users such as age, gender, movie time, history of the previous movies watched, etc. K -means clustering aims to group the features into clusters that represent the characteristics of the group [ 31 ]. If the classification is based on age, the probable K -means clustering will use children, teens, youth, and adult clustering methods. If a client falls within any of these age groups, movies are recommended based on what other people within that age group do. If the clustering depends on age, the closer an age is to the centroid age, the better the classification recommendation. The steps in the classification are measuring the similarity between the user and item features, selection of the neighbours, computing the prediction, and suggesting it [ 14 ].

4.1.1. Measurement of the Similarities

The first step is finding the similarity in the user features that the new user has with the previous system users. The algorithm always has the basic classifications for a beginning, where the user can give inputs and the predictions can be made [ 32 ]. Common features in finding the similarities are age, previous history, and geographical locations. Other recommender systems in movie theatres, including the price, the time to watch the movies, etc., are used in coming up with the means (centroids) for clustering. The distance from the centroids can be based on a Pearson correlation, cosine-based similarities, or an adjustment of the cosine-based similarity. The calculation of the similarity may be item-based or user-based. Item-based computation finds the similarities based on the features in the movies that similar people liked. If it is user-based, the calculation of the centroids is based on the demographic features of the user [ 15 ].

The computation of the similarities between items or users is shown in the mathematical equations below:

The equation above computes the correlation between the user and the item; it computes the closeness of the value to the centroid value. It is assumed that the two items i ∩ j are the correlated features (items or users); the value r j ¯ is the centroid feature, while the value r i is the value of the new user or new feature to be compared through correlation [ 33 ].

4.1.2. Selection of the Neighbours

There is always a consideration when developing the algorithm. The key metrics are the accuracy to obtain and the running time of the algorithm. To increase the accuracy of an algorithm, a large number of neighbours, which increases the computational time of the algorithm, is required. If a smaller computational time is needed, accuracy will be compromised [ 34 ]. To strike a balance, the selection may be threshold-based or use the top-N technique. The threshold technique will run only a specific number (sample number that meets the threshold value) of assessments of the neighbours and predict if that threshold is reached. For example, if the population is 1000, the system will run a prediction from 100 samples and predict out of the 100 samples [ 35 ]. In the top-N technique, only the top number of similarities (N) is run rather than the whole population of neighbours. For example, it will select only the top 10 for suggestions based on the nearest neighbours rather than assessing the whole population [ 36 ].

4.1.3. Prediction Computation

The computation of the subsequent predictions is based on the closest neighbours found in the system database. The prediction is obtained by the formula below:

The prediction or the nearest neighbour to the centroid ( K -mean) is made. In the equation above, the K -means is represented by r u ¯ while the correlation of the other variable on the right-hand side of the equation gives the nearest neighbour, both used in making the suggestion prediction [ 27 ].

4.1.4. Limitations of K -Means Clustering

Cold-Start Problem : This is a prediction problem that happens with a new user to the system. There is very little information about the user; hence it is difficult for the system to make any predictions until the user starts feeding some information that can be correlated to and suggestions made based on the user or previous item characteristics. The negative impact is the system accuracy is greatly reduced [ 6 ]. It is because of this problem that new and excellent movies are not recommended to users, or new users do not find what is best for them.

Sparsity in the dataset : The recommendation system involves assessing a large amount of data in the movie database. The users only look for a few items in the database; they are not able to use and assess a significant portion of the database to effectively evaluate the features. Apart from that, the users do not rate the movies they watched in the system. It becomes hard for the system to determine if the user liked the movie they watched, or they never liked it because they never left any rating. The negative impact is leaving some of the best movies not recommended in the large dataset since they have not been rated by the user. Moreover, the threshold/top-N techniques leave out the best matching suggestions [ 37 ].

Scalability : One of the challenges cited in the selection of the neighbours was balancing the computational time and the accuracy of the system. The K -means filtering technique is accurate when the database has a small number of movies to recommend or few users [ 38 ]. However, with an increase in the number of users and the number of movies, the computational time or the threshold number of items increases; therefore, the computational time increases [ 39 ]. To overcome this disadvantage, computation and training of the algorithm are performed offline so that when the systems are back online, recommendations are made easily [ 40 ].

The K -means filtering algorithm is the most basic collaborative filtering technique. It is from this technique that other filtering concepts are developed. The computation technique to arrive at the predictions may not be the same; the mode of working mimics the K -means nearest neighbour [ 41 ]. The other algorithms are developed to overcome the K -means clustering limitations.

4.2. Principal Component Analysis K-Means

This is a content-based movie filtering technique that improves on the K -means clustering technique. The major components in the movie are used to classify the movies before recommendations can be made to the customers. The K -means algorithm calculates the closeness of a feature to the centroid using the distance from the mean point. However, the principal component analysis creates a covariance matrix to calculate the eigenvectors and eigenvalues [ 42 ]. Therefore, it widens the scalability to find better comparisons to make the movie suggestions [ 43 ]. To illustrate this, assume a K -means algorithm computes the similarity of a single feature at a time. This implies the computational time and accuracy are compromised. However, using PCA, a covariance matrix of various features is created; hence the scalability is increased and computed faster. If there are similarities that fall within the matrix, they can be found easily, and its eigenvector is computed. Suggestions close to such an eigenvector are then made to recommend the movies [ 44 ].

Steps in Conducting Principal Component Analysis

Data formulation: This is the first step where data is formulated and structured into tuples of dimension m × n. The possible tuples may depend on the user characteristics, the movie feature characteristics, or the combination of the user and feature characteristics [ 42 ]. The structure of the tuples is as given below in Table 2 .

Structure of the tuples.

Where r i is the movie rating, u i is the user characteristics, and i i is the item characteristics (movie characteristics). A and B will give a 2D matrix of dimension m × n matrix, while C will give a 3D matrix of dimension m × n × o .

Calculation of the covariance matrix: A covariance matrix of the dimension of the data formulated in the previous step is computed.

Calculation of the eigenvectors and eigenvalues : The covariance matrix calculated will be a square matrix of the dimension of the data. It is used to compute the eigenvalues and eigenvectors which characterize the data. The computed eigenvectors are sorted in decreasing order according to the eigenvalues; a future vector is constructed [ 45 ].

4.3. Principal Component Analysis Self-Organizing Maps (PCA-SOM)

Self-organizing maps (SOMs) is a technique based on neural networks; it is an unsupervised learning technique, and there is no need for intervention of humans during the learning phase. It is vital in clustering data without knowing the class memberships in the input data [ 46 ]. The self-organizing feature map (SOFM) is known for detecting the features inherent in particular items which is important for the features in the movie recommender systems. SOM also uses topology-preserving mapping, which implies that the algorithm preserves the relative distance between all the points in the initial dataset [ 47 ]. Therefore, it effectively achieves the objective of transforming the arbitrary dimensions into a 1D or 2D discrete map. PCA is integrated with SOM because it is easier for the PCA to convert the matrices generated by SOM to eigenvectors and eigenvalues for ranking in the order of significance [ 48 ]. The steps in working out PCA-SOM are listed below:

Obtain data without any rankings or classifications;

Data modelling;

SOM classified the data using unsupervised learning to bring together that which has similarities in features;

PCA takes over from the classification achieved by SOM, checks the principal components, and comes up with further classifications of the dataset;

The decision to make the suggestion.

Initialization: Once the data are obtained, random values are chosen for the weight of the initial vectors. The weights of the vectors represent the neurons in the data, and their values are also computed [ 49 ].

Sampling: A known sample x is drawn from the input space with a known probability. This is the activation pattern that is applied to the lattice. This pattern maps the x dimension to be proportional to the m-pattern in the new lattice [ 49 ].

Similarity Matching: The best matching is found at time step-n using the minimum Euclidean distance between the neuron centroid.

Updating: The synaptic weight of the neurons is adjusted using the formula below:

where η n is the learning rate, h j i x n is the neighbourhood function of i x winner neuron. These two are dynamic to obtain the optimum results.

Apply PCA: After the synaptic weights are derived from the minimum Euclidian distance from the formula above, the PCA process in creating the eigenvalues and eigenvectors is used further in processing the data to obtain a more accurate estimation [ 50 ].

Decision: After similarities are matched, the suggestions are made.

The great features of SOM that make it a good tool in recommender systems are:

Insights into the input space: The method uses unsupervised learning to classify the data by weight vectors and give output in a feature map. The cold starting is significantly reduced [ 51 ]. The user can then input data in light of the initial output features shown.

Topological arrangement: The feature map of SOM works by mapping the field of the input pattern to a spatial location in the output grid [ 52 ].

Density Matching: Once the input is fed into the system, any alterations in input distribution are equally represented in the output grid so that there will be a good representation of the highest density areas with the most matches and lower density areas with fewer matches [ 49 ].

Feature Selection: The SOM algorithm selects the best attributes for the non-linear distribution in the input data so that it can effectively match the similarities to the grids [ 50 ].

4.3.1. Advantages

Since it is based on unsupervised learning, it automatically updates the features and functions [ 53 ]. It is flexible to new input because it learns by itself. It is suitable for unidentified new inputs, for example, new movies that have no ratings or new users where there is no data about them. The new movies may be recommended when the system extracts their features, and the new users will not experience a cold start because they have somewhere to begin on the output feature map [ 54 ]. It is also faster in computation since it easily organizes complex data and makes a good representation of the mapping for easy interpretation.

4.3.2. Disadvantages

The major drawback is that feature classification may not be according to the expected output; therefore, the unsupervised learning classification algorithms have to be initialized often to maintain the relevance of the clustering [ 55 ].

5. Metaheuristic Algorithms for Movie Recommendation Systems

Metaheuristic algorithms are high-level methods or heuristics which have been developed to search, create, or select a heuristic that may produce a satisfactory solution for optimization problems. Metaheuristics find wide usage in almost all aspects of optimization problems. For example, metaheuristics have been used in design optimization [ 56 , 57 , 58 , 59 ], process optimization [ 60 , 61 , 62 , 63 ], structural optimization [ 64 , 65 ], knapsack problems [ 66 , 67 ], workflow scheduling [ 68 ], image segmentation [ 69 , 70 , 71 ], etc.

5.1. Genetic Algorithm

This is a hybrid filtering algorithm that uses the improved K -means clustering and is combined with the genetic algorithm (GA). It uses the PCA technique to partition the high dimensional space into clusters hence reducing the complexity of computations when making intelligent recommendations. The method has higher performance characteristics; hence it makes better recommendations. The steps of the recommendation system are outlined below:

5.1.1. Data Preprocessing Using PCA

The first step is processing the data, extracting it from the original high dimensional space into a linear relatively low space with denser features that carry the information. The PCA feature extraction technique has been very effective. It combines the data represented by the principal component with the highest eigenvalue with the significant information after ranking them. The components with lower significance are ignored but components with higher significance are given prominence. After the linear reduction, only a selected number of components from the rank is fed to the GA-KM algorithm for classification.

5.1.2. Enhanced K -Means Clustering Optimization by Generic Algorithms (GA-KM)

The objective is to make sure that the users/neighbours with like-minded interests or features are grouped. Therefore, it performs it in two stages which are K -means clustering and GA algorithms.

5.1.3. K -Means Clustering

The technique, as discussed, centres its clusters around centroids based on the linear distance from the central feature. The correlation of distance from the central point determines the similarity index. If it is too similar, there is convergence; if there is a high dissimilarity, then the dataset is sparse. As discussed, it suffers a cold start, and its first centroid may be based on the local optimum rather than the global optimum. The steps in K -means clustering are selecting the centroids, assigning objects to the closest clusters, computing the sum of squared distances from the members in the cluster, and checking for convergence in the computed objects. The procedure for computation is similar to that discussed.

5.1.4. Genetic Algorithm

This mimics biological evolution as explained by Darwin’s theory of evolution. The algorithm uses the population of individuals as chromosomes; the chromosomes represent possible solutions to the evolution problem [ 29 ]. Each of the chromosomes contains the genes with the survival ability. Therefore, through natural selection, the chromosomes with the highest quality genes have the highest chance of survival and are fit for reproduction for the next generation. The iterations are based on selection, crossover, and mutation. Selection picks just a proportion of the genes to breed for the next generation. Crossover swaps two parent chromosomes to be recombined into the offspring. Mutation randomly alters the value of a gene to produce offspring. The processes extend the diversity of the offspring. The processes end when the fitness conditions in the environment/context are met.

The GA algorithm is used to prevent premature convergence in the K -means algorithms. The centroids in K -means are considered the chromosomes; the fitness function to evaluate the quality of the solution is:

The fitness value is the sum of the distances of the inner points to the cluster centres. The values are minimized to find the optimal partitions. To find the optimal partitions, the three generic operators precede the construction of the offspring based on the survival fitness principles; convergence occurs when the fitness criterion is satisfied [ 72 ]. The pseudocode of the algorithm is summarized below:

Initialization

Parameter initialization: Set the maximum iterations, population size, cluster numbers, probability crossover, probability mutation, and fitness function to minimize the total distance of every sample to its nearest centre;

Population initialization: Randomly generate the initial population for each of the k -centers.

Selection operation;

Cross-over operation;

Mutation operations;

Obtain the initial k -centres with optimal fitness values;

K -means optimization: generate new clusters with k -centres.

When tested with the MovieLens dataset, the algorithm has better performance features especially in reducing the cold-start problem [ 29 ].

5.2. Firefly Algorithm

The algorithm is also bio-inspired from the fireflies and combines it with a fuzzy C -means clustering technique. In the natural world, the fireflies are pulled to the brightest firefly using the light signal. Each firefly pulls the other, but the brightest has the highest attractiveness, and other fireflies are clustered around it. Similarly, the algorithm centres its suggestion on features of the users with the highest attractiveness (highest user ratings) [ 55 ]. If a movie has the highest rating from many users, the movie recommender system will make subsequent recommendations based on movies rated highest by users with similar characteristics [ 73 ]. The algorithm for the recommender system is highlighted below:

All the fireflies are unisexual, and every firefly pulls to another firefly.

The attractiveness of a firefly depends on its brightness, and the other fireflies will be pulled closer to the brighter one (feature reduction using the firefly algorithm).

This brightness is related to a primary function in the FCM.

FCM allocates memberships and utilizes them to show data elements from one cluster to another.

The recommender system efficiency and performance are generally higher than the traditional K -means clustering.

5.3. Artificial Bee Colony

This is a bioinspired algorithm that makes recommendations based on the workings of the bees in finding flowers for the best nectar [ 74 ]. The bees are mainly divided into two groups. Scouting bees go out to scout flowers with the best nectar, and the employee bees follow after the best flowers have been found [ 53 ]. It is worth noting that several scouting bees are sent out and come back with information to the hive regarding the quality of the nectar found. The employee bees will filter out the low-quality nectar from the information and follow the scout bee to the source of the best nectar. Similarly, the artificial bee colony in recommender systems works as an improvement of the K -means clustering algorithm [ 75 ]. In the K -means algorithm, an assumption is made that the data is based on a centroid where the closeness of the feature to the centroid feature determines the recommendation. In an artificial bee colony, there are many centroids (just as there are many flowers), and information from or to these centroids will bring a variety. From this variety, the user may choose what is best suited for them; henceforth, the recommendation system will bring recommendations close to the centroid chosen [ 76 ]. It is a good method to solve the sparsity, scalability and cold-starting problem. The user will choose the best suited feature from the first random set of options available. Subsequent recommendations will depend on the K -means around that particular choice. The steps are summarized below:

Initialize the system users and movies in a matrix;

Use the K -means clustering to find several centroids of various product features. This finds several centroids for clustering;

Selection of the nearest clusters;

Calculation of the estimated rating values from the user history;

Use the artificial bee colony to select the closest to user likes based on ratings and features;

Reclassification of the users for further iterations;

Coming up with the recommendations.

ABC determines the community of vectors that explore the similarities in the neighbours. The objective function is then continually reduced when narrowing down to the nearest possibilities [ 77 ]. The aim is to minimize the objective function below:

The objective function is controlled by succeeding iterations determined by the detecting vector z → as below:

From the succeeding iterations, the points with the most similar features are selected, and the system recommends the movies to the user [ 78 ]. For instance, an initial allocation of the centroids may be classified as horror movies, thrillers, comedy, or thrillers. If the client selects thriller movies, a further classification may be Hollywood thriller, Bollywood thriller, etc. If the client selects any from these, the subsequent recommendations will be based on this particular centroid [ 74 ]. As seen, it has an advantage because there are always initial centroids that the user can select that further narrow down the selection. Optimization is reduced by the detecting vector to optimize the suggestion to the most viable suggestions [ 79 ].

5.4. Cuckoo Search

The cuckoo search algorithm is a combination of K -means clustering and the use of Levy’s flight function. In this process, the K -means algorithm divides the MovieLens Dataset into different clusters. This is performed using randomly selected centroids [ 14 ]. Measures such as the Euclidian distance and cosines are used to find the distance between centroids, and the features and/or users are reassigned to the closest cluster with similar characteristics.

The cuckoo search algorithm gets its inspiration from the cuckoo bird. The cuckoo bird does not sit on eggs to hatch; rather, it searches for the best nest with optimal conditions and lays eggs for the host bird to sit on to hatch [ 80 ]. If the host bird identifies the egg, it may throw it away or abandon the whole nest. Similarly, in the recommender algorithm, if the centroid does not present the optimal solution, the centroid is abandoned for a new iteration until no re-assignment happens. The pseudocode for the recommender system is outlined in the procedure below:

5.4.1. K -Means Clustering

Initialize the number of k clusters;

Random selection of centroids using K -means clustering;

While no centroid is changed, assign each data point to the closest centroid and calculate the new centroids;

Assign data points to the closest cluster mean.

5.4.2. Cuckoo Search Algorithm

Begin the fitness function f x ,   X i =   x 1 ,   x 2 … ;

Initialize the random population of n host nests (centroids from K -means);

Calculate the fitness function value for each nest;

Find the ith cuckoo randomly by Levy flights, and calculate its fitness, Fi ;

Select a nest (centroid);

If ( F j > F i ) ; replace j with the new solution;

The unqualified nests (centroids) are abandoned and new ones built by Levy flights function;

The best solutions found are ranked and suggested to the client.

The cuckoo search algorithm has higher precision, recall, and a lower MAE than the PCA- K -means, hence higher performance characteristics. The computations can also be performed offline so that recommendations are made when the system is online, making it faster to make suggestions to the user.

5.5. Grey Wolf Optimizer

This is a recommendation system that is based on mimicking the leadership and hunting tactics of the grey wolfs [ 81 ]. The algorithm first conducts feature selection using the grey wolf optimizer (GWO) method, before clustering using the FCM method [ 82 ]. The algorithm pseudocode is listed below:

Load the GWO culture;

Initialize the coefficient points r, Q and R;

The appropriateness of each explorer is estimated X α   X β   X δ ;

Carry out iterations to determine the appropriateness of entire explorer negotiators;

Return X α representing the position of centroids by GWO;

Randomly select the cluster centres based on fuzzy means;

Determine midpoints B (k) = [ c m ] with F (k) ; continue with iterations until ||F( k + 1) − F( k ))) < ε;

Return to the newly formed cluster centres and make recommendations based on the cluster centres.

This recommender system has a relatively better performance.

5.6. Other Metaheuristic Algorithms

Some researchers have used other metaheuristic algorithms to develop movie recommender systems. For example, Papneja et al. [ 83 ] developed a movie recommendation using a whale optimization algorithm. Tripathi et al. [ 84 ] hybridized a map-reduce-based tournament along with a WOA to achieve a superior recommendation experience.

6. Model Metrics

Various aspects have to be measured apart from the accuracy to make sure that the algorism makes the right predictions. For example, the algorithms may be highly accurate but have too much logarithmic loss. Accuracy is not the only metric to determine the performance efficiency of a model. The metrics are discussed below:

6.1. Mean Absolute Error (MAE)

This is the average difference between the predicted values and the original values. In our case, it is the average difference between the choice of the movie by the customer (user) from the suggestion made (prediction). It gives the variation between the suggestion and what the customer chose. The only disadvantage is it does not give the direction of the error [ 85 ]. Generally, a low mean absolute error is desirable. The mathematical formula for MAE is shown below:

where n s is the number of samples, y ¯ i is the predicted suggestion, and y i is the true feature that the user picks/wants.

6.2. Mean Squared Error (MSE)

This gives the square of the MAE (the square of the average difference between the original values and the predicted values). The advantage is that it makes the large errors more pronounced so that the model focuses on the large errors and their causes [ 86 ]. In addition, it is easier to model the linear programming models in the computation of the slope using the mean absolute error since the differences will be clearer. The formula for the MSE is shown below:

6.3. Log Loss

This is a cross-entropy loss given by probability estimates. It is used in neural networks and recommender system optimizations. It calculates the probability of the suggestions rather than giving only discrete predictions, especially during the ranking of the suggestions [ 35 , 87 ].

6.4. Confusion Matrix

This is one of the most used metrics in determining the accuracy of a model. It is mainly used for classification problems, especially when the outputs expected should have more classifications [ 34 ]. The various characteristics of the confusion matrix are shown in Table 3 .

Characteristics of a confusion matrix.

As noted earlier, the movies are clustered based on the features or the users. In clustering, the true represents the actual classification of the movie, while the predicted gives the predicted classification of the movie before recommendation [ 88 ]. For example, a movie may be classified as a comedy when it is a thriller movie. The user may choose to think it is a comedy because of the characters only to find it is a thriller movie. The movie may be classified as a thriller, and it is a thriller; hence the users get what they want. Such variations happen in the movie classification; hence there is a need for accurate predictions.

True Positive: This points to a case in the recommender system where the actual suggestion was positive, and the client selection of the movie was positive, i.e., the system suggests what the client needed. An example is when the movie is classified as a comedy when the client needed comedy and selects it [ 89 ].

True Negative: This happens when the actual classification is negative, and the prediction is also negative. In our movie recommendation example, if the movie is not a comedy and our algorithm does not classify it as comedy, this output is termed as true negative [ 90 ].

False Positive: This happens when the actual classification is negative, but the system predicts it as positive [ 91 ]. In the movie recommendation system example, the actual case may be the movie is not a comedy; yet the prediction algorithm classifies it as comedy. The actual movie is not a comedy, hence negative, but the prediction is comedy, hence the term positive.

False Negative: This happens when the actual data is true (positive), but the prediction is negative (false) [ 87 ]. The actual classification is true; yet the system predicts it as negative. In the movie recommender systems, the actual movie is a comedy, but the system predicts not comedy.

6.5. Precision

This assesses how many of the true positives are true positives. It gives a fraction of the true positive predictions to the total positive predictions [ 92 ]. The mathematical format is shown below:

6.6. Recall/Sensitivity

This is used as the fraction of true values from the total true values [ 35 ]. In our recommender systems, it gives a fraction of what is classified as comedy out of a total of what is comedy. The fraction of the true values out of the total true values is mathematically modelled as below. Note that false negative is true, but the algorithm classified it as else:

Precision focuses on capturing the classifications correctly, while recall focuses on whether the system is able to capture the features we want though they may not be correctly captured [ 34 ].

6.7. Accuracy

This is the fraction of correct predictions with the total predictions. The accuracy is mathematically modelled as below:

Accuracy as a metric should be used only when the data are balanced and have various classes [ 93 ]. It should not be used as a metric when the data are skewed (have a majority of only one class). For example, if the data are made of 100 movies, and only 5 movies are comedies, the rest are different genres such as thrillers. If the algorithm wrongly predicts all the movies as thrillers, it will be 95% accurate because 95% are thrillers, and only five are comedies. However, from a rational standpoint, the algorithm failed to classify comedy movies. In a recommendation system, the client would think that none of the movies in the datasets is comedy and not watch; yet there are five top-rated comedy movies. Therefore, accuracy should be used when the dataset is well-balanced.

This is simply the harmonic mean of precision and recall. It shows how precise the system was and how it never missed significant instances. If the F 1 score is high, the model performance is high [ 94 ]. The mathematical formula for precision is shown below:

6.9. Computational Time

This is the time that the algorithm takes to come up with the final solution in the prediction. If the systems take long, they may be unreliable if the users want an immediate response before they select the movies to watch. The algorithms should ensure that the best results are found within the shorted period possible [ 53 , 95 ]. A high-performing system returns the most efficient results within a relatively short period. It increases its reliability and dependency. Sometimes the data to be analyzed may be too large to give immediate results. To overcome this limitation, the computations are performed when the system is offline so that the output is shown when it is back online for effective prediction suggestions [ 96 ].

7. Problems Associated with Movie Recommender Systems

7.1. cold start.

The best target audience for a recommendation always depends on the previous user characteristics and the features of the products they watched. A comparison is always made on the characteristics of the user was and the features of the movie and the rating given to the movie. However, in some instances, there are no user characteristics that can be used for a recommendation if the user is new, and nothing is known about them [ 97 ]. Sometimes the user may not be new but has used a different device when accessing the movies’ websites hence there are no stored cookies that can trace the user history.

A cold-start problem occurs when the recommender system is not able to make any suggestions to the user because the user is new or there is no information available about the user [ 98 ]. The problem is common in collaborative filtering which uses only user details to make recommendations of the best movie. The problem is overcome by using content-based filtering, context-based filtering and hybrid filtering. In content-based filtering, the movies are classified by the features such as the main characters, the genre etc. The new user will select any genre based on the content. A context-based filter is based on some of the user information derived from the device such as location, and the operating system, and correlates them with what other users from similar contexts are using. In hybrid filtering, the content, context, and user characteristics are used, therefore, if the recommender system does not have any information about the user, it will use the content and context to make the first recommendations [ 99 ]. The subsequent recommendations will depend on the hints of information available.

7.2. Accuracy

If the database for the recommender systems has few movies, the system will have higher accuracy. If the database is large, there tends to be a lower accuracy because the pool of information searched is too large. To counter the problem, the K -means algorithm reduces the computational time by restricting the computation to a certain number of iterations or selecting only the top-N number of movies for recommendations [ 100 ]. However, if some of the movies have never been rated, they are likely to be biased in the searches [ 101 ].

To increase the system accuracy in making the recommendations, some of the algorithms have employed sophisticated search criteria that will conduct a thorough search and match the product features to user and item characteristics [ 102 ]. In addition, two or more algorithms are combined to allow the perfect user and feature analysis and come up with the desired output. Some of the classification processes such as the PCA-SOM conducts the logarithmic computations offline so that they give recommendations easily when they are online. It reduces the computational time and increases the recommender system accuracy. In modern recommender systems, cold-start problems and the accuracy is solved by having a dialog box where the users can type in the features they need, and recommendations will be given according to what matches the search words [ 103 ].

7.3. Diversity

New movies in the recommender systems rarely appear among those that are suggested to the users. The new excellent movies may end up not being watched because of the lack of being rated by the users. Some of the excellent movies also may not be rated, leaving the recommendation system blank about whether the movies are great for a specific class of watchers or not. To overcome some of these challenges for new movies and movies that are not rated by the users, the diversity aspect is introduced by the recommender systems. In diversification, the new movies or unrated movies are given priority so that they can be noticed by the users [ 104 ]. If they are pleasing, they will be rated and watched more. From this information, the recommender systems will make subsequent decisions on whether to recommend the movie or archive it. From the number of watches, it will also classify the movie according to the features or user characteristics [ 102 ]. Diversity is often used to recommend new debut movies to increase their marketability and presence. It increases the diversity of the user to try out new features or new products.

7.4. Scalability

While sparsity and diversity aim to increase the chances of movies with new features appearing in top searches, scalability aims to solve the problem of increased computational time and increase the performance of the recommender system. Scalability ensures that there is a balance obtained between accuracy and computational time. If it is necessary, some of the classification computations are performed beforehand so that by the time the user comes to select an item to watch, the system makes an almost immediate recommendation with high levels of efficiency [ 105 ].

7.5. Sparsity

Sparsity in the movie data relates to the large volume of movie data in the system, but the users only utilize a few of the features or resources. It is common in K -means clustering where the data is interpolated linearly and gives fewer perspectives to the non-linear data [ 106 ]. Recommendation systems may sometimes be biased by only suggesting the most rated or the most liked movies based on a limited assessment of all the possible cluster features. By using the top-N theory to make the recommendation, those that do not meet the threshold of these can find better algorithms that consider the sparsity of information available [ 107 ]. Some of the methods such as PCA-SOM map all the features on a lattice; hence the user can find most of the features. WOA also widens the scope of the search by using both linear and orthogonal systems to find the desirable features and make recommendations that are sparser and more diverse. Generally, implementing systems that consider non-linearly related data is efficient [ 108 , 109 ].

8. Discussions

The current movie recommendation systems have to work in contexts where there is so much data to be considered before making recommendations. Both user and context information are so varied that the accuracy and precision of the systems are brought to real tests. For example, most of the user information is shared through social media platforms to generate interest in the movies. The MovieLens dataset was created approximately 20 years ago when there was little or no developments in the use of social media where users share movie information to create interest. However, current technologies need to analyze the content, context. and user characteristics in social media platforms to recommend the right movies to the customers. Some companies have taken steps to integrate analytics in their recommender system algorithms. They ask the customer to connect to their social media accounts such as Twitter, YouTube, and Meta not only for advertising but also to analyze the activity of the user on these social media accounts to recommend the best movies for them. Through connecting to these platforms, they analyze the previous history of the user and recommend appropriate movies. This significantly reduces the cold-start problem since new user information can be obtained.

Context-based filtering is gaining traction in the movie recommender systems. It has been adequately used in product recommendations on e-commerce platforms., for example, the most discounted products during black Fridays, the holiday products during Christmas seasons, etc. Movie recommender systems that integrate time stamps to recommend the best movies in various contexts should be studied and developed. For example, it will help recommend movies for children learning during the day and children lullaby movies when it is time to sleep at night.

There are various advances in the use of blockchain technology, and some of these applications may affect the efficacy of algorithms in movie recommender systems. Blockchain technology enhances user privacy through user data encryption; yet collaborative filtering depends on the availability of user information so that it can match the features and characteristics before making recommendations. If user information is concealed by the blockchain systems, the algorithms have to use advanced methods to prevent a decline in the accuracies such as the use of context and content-based filtering.

9. Conclusions

In this article, movie recommender systems have been described and classified. The various types of recommender systems are introduced and discussed. Special emphasis is given to explain in detail the various machine learning and metaheuristic algorithms commonly deployed in movie recommendation research. The various model metrics that summarize the quality of the model are discussed at length. The problems associated with movie recommender systems are also summarized in a structured way and discussed. A total of 77 articles strictly on the area of movie recommender systems are included in the study, and their major conclusions are presented. In addition, 32 other related articles on metaheuristics and recommender systems (not for movies) are also introduced in various sections to present a coherent and meaningful review. One of the limitations of the study is that the Scopus and Web of Science databases were not directly used for selecting the articles for review. In contrast, EBSCO Academic Search Premier, ScienceDirect, IEEE Library, ResearchGate, SpringerLink and the ACM Portal were used for the literature search. Nevertheless, more than 80% of the reviewed papers were found to be indexed in Scopus while more than 60% were available in the Web of Science database.

Author Contributions

Conceptualization, S.J., N.G. and R.Č.; data curation, S.J., N.G. and J.S.M.; formal analysis, S.J., N.G., R.Č. and J.S.M.; investigation, J.S.M.; methodology, S.J., N.G. and R.Č.; supervision, R.Č.; writing—original draft, S.J., N.G. and J.S.M.; writing—review and editing, R.Č. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Funding Statement

This research received no external funding.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

• 1. Alyari F., Navimipour N.J. Recommender systems: A systematic review of the state of the art literature and suggestions for future research. Kybernetes. 2018;47:985. doi: 10.1108/K-06-2017-0196. [ DOI ] [ Google Scholar ]
• 2. Caro-Martinez M., Jimenez-Diaz G., Recio-Garcia J.A. A theoretical model of explanations in recommender systems; Proceedings of the ICCBR; Stockholm, Sweden. 9–12 July 2018. [ Google Scholar ]
• 3. Gupta S. A Literature Review on Recommendation Systems. Int. Res. J. Eng. Technol. 2020;7:3600–3605. [ Google Scholar ]
• 4. Abdulla G.M., Borar S. Size recommendation system for fashion e-commerce; Proceedings of the KDD Workshop on Machine Learning Meets Fashion; Halifax, NS, Canada. 14 August 2017. [ Google Scholar ]
• 5. Aggarwal C.C. Recommender Systems. Springer; Berlin/Heidelberg, Germany: 2016. An Introduction to Recommender Systems; pp. 1–28. [ DOI ] [ Google Scholar ]
• 6. Ghazanfar M.A., Prugel-Bennett A. A scalable, accurate hybrid recommender system; Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining; Washington, DC, USA. 9–10 January 2010. [ Google Scholar ]
• 7. Deldjoo Y., Elahi M., Cremonesi P., Garzotto F., Piazzolla P., Quadrana M. Content-Based Video Recommendation System Based on Stylistic Visual Features. J. Data Semant. 2016;5:99–113. doi: 10.1007/s13740-016-0060-9. [ DOI ] [ Google Scholar ]
• 8. Alamdari P.M., Navimipour N.J., Hosseinzadeh M., Safaei A.A., Darwesh A. A Systematic Study on the Recommender Systems in the E-Commerce. IEEE Access. 2020;8:115694–115716. doi: 10.1109/ACCESS.2020.3002803. [ DOI ] [ Google Scholar ]
• 9. Cami B.R., Hassanpour H., Mashayekhi H. A content-based movie recommender system based on temporal user preferences; Proceedings of the 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS); Shahrood, Iran. 20–21 December 2017. [ Google Scholar ]
• 10. Beniwal R., Debnath K., Jha D., Singh M. Data Analytics and Management. Springer; Berlin/Heidelberg, Germany: 2021. Hybrid Recommender System Using Artificial Bee Colony Based on Graph Database; pp. 687–699. [ DOI ] [ Google Scholar ]
• 11. Çano E., Morisio M. Hybrid recommender systems: A systematic literature review. Intell. Data Anal. 2017;21:1487–1524. doi: 10.3233/IDA-163209. [ DOI ] [ Google Scholar ]
• 12. Schafer J.B., Konstan J.A., Riedl J. E-commerce recommendation applications. Data Min. Knowl. Discov. 2001;5:115–153. doi: 10.1023/A:1009804230409. [ DOI ] [ Google Scholar ]
• 13. Shen J., Zhou T., Chen L. Collaborative filtering-based recommendation system for big data. Int. J. Comput. Sci. Eng. 2020;21:219–225. doi: 10.1504/IJCSE.2020.105727. [ DOI ] [ Google Scholar ]
• 14. Dakhel G.M., Mahdavi M. A new collaborative filtering algorithm using K-means clustering and neighbors’ voting; Proceedings of the 11th International Conference on Hybrid Intelligent Systems (HIS); Malacca, Malaysia. 5–8 December 2011; pp. 179–184. [ DOI ] [ Google Scholar ]
• 15. Katarya R., Verma O.P. An effective collaborative movie recommender system with cuckoo search. Egypt. Inform. J. 2017;18:105–112. doi: 10.1016/j.eij.2016.10.002. [ DOI ] [ Google Scholar ]
• 16. Kumar B., Sharma N. Approaches, Issues and Challenges in Recommender Systems: A Systematic Review. Indian J. Sci. Technol. 2016;9:1–12. doi: 10.17485/ijst/2015/v8i1/94892. [ DOI ] [ Google Scholar ]
• 17. Colomo-Palacios R., García-Peñalvo F.J., Stantchev V., Misra S. Towards a social and context-aware mobile recommendation system for tourism. Pervasive Mob. Comput. 2017;38:505–515. doi: 10.1016/j.pmcj.2016.03.001. [ DOI ] [ Google Scholar ]
• 18. Chou J.-S., Bui D.-K. Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy Build. 2014;82:437–446. doi: 10.1016/j.enbuild.2014.07.036. [ DOI ] [ Google Scholar ]
• 19. Casillo M., Conte D., Lombardi M., Santaniello D., Valentino C. Recommender System for Digital Storytelling: A Novel Approach to Enhance Cultural Heritage; Proceedings of the Pattern Recognition, ICPR International Workshops and Challenges, ICPR 2021; Virtual. 10–15 January 2021; pp. 304–317. [ DOI ] [ Google Scholar ]
• 20. Baltrunas L., Ricci F. Experimental evaluation of context-dependent collaborative filtering using item splitting. User Model. User Adapt. Interact. 2013;24:7–34. doi: 10.1007/s11257-012-9137-9. [ DOI ] [ Google Scholar ]
• 21. Baltrunas L., Kaminskas M., Ludwig B., Moling O., Ricci F., Aydin A., Lüke K.-H., Schwaiger R. Incarmusic: Context-aware music recommendations in a car; Proceedings of the International Conference on Electronic Commerce and Web Technologies; Vienna, Austria. 4–5 September 2011. [ Google Scholar ]
• 22. Baltrunas L., Ludwig B., Peer S., Ricci F. Context relevance assessment and exploitation in mobile recommender systems. Pers. Ubiquitous Comput. 2011;16:507–526. doi: 10.1007/s00779-011-0417-x. [ DOI ] [ Google Scholar ]
• 23. Casillo M., Gupta B.B., Lombardi M., Lorusso A., Santaniello D., Valentino C. Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias. Electronics. 2022;11:1003. doi: 10.3390/electronics11071003. [ DOI ] [ Google Scholar ]
• 24. Baltrunas L., Ludwig B., Peer S., Ricci F. Context-Aware Places of Interest Recommendations for Mobile Users; Proceedings of the International Conference of Design, User Experience, and Usability; Orlando, FL, USA. 9–14 July 2011; pp. 531–540. [ DOI ] [ Google Scholar ]
• 25. Casillo M., De Santo M., Lombardi M., Mosca R., Santaniello D., Valentino C. Recommender Systems and Digital Storytelling to Enhance Tourism Experience in Cultural Heritage Sites; Proceedings of the IEEE International Conference on Smart Computing (SMARTCOMP); Irvine, CA, USA. 23–27 August 2021; pp. 323–328. [ DOI ] [ Google Scholar ]
• 26. Casillo M., Conte D., Lombardi M., Santaniello D., Troiano A., Valentino C. A Content-Based Recommender System for Hidden Cultural Heritage Sites Enhancing; Proceedings of the Sixth International Congress on Information and Communication Technology; London, UK. 25–26 February 2021; pp. 97–109. [ Google Scholar ]
• 27. Park D.H., Kim H.K., Choi I.Y., Kim J.K. A literature review and classification of recommender systems research. Expert Syst. Appl. 2012;39:10059–10072. doi: 10.1016/j.eswa.2012.02.038. [ DOI ] [ Google Scholar ]
• 28. Arulmozhivarman M., Deepak G. OWLW: Ontology Focused User Centric Architecture for Web Service Recommendation Based on LSTM and Whale Optimization; Proceedings of the European, Asian, Middle Eastern, North African Conference on Management & Information Systems; Istanbul, Turkey. 19–20 March 2021; pp. 334–344. [ DOI ] [ Google Scholar ]
• 29. Wang Z., Yu X., Feng N., Wang Z. An improved collaborative movie recommendation system using computational intelligence. J. Vis. Lang. Comput. 2014;25:667–675. doi: 10.1016/j.jvlc.2014.09.011. [ DOI ] [ Google Scholar ]
• 30. Vilakone P., Park D.-S., Xinchang K., Hao F. An Efficient movie recommendation algorithm based on improved k-clique. Hum. Cent. Comput. Inf. Sci. 2018;8:38. doi: 10.1186/s13673-018-0161-6. [ DOI ] [ Google Scholar ]
• 31. Cho Y.S., Moon S.C., Noh S.C., Ryu K.H. Implementation of personalized recommendation system using k-means clustering of item category based on RFM; Proceedings of the 2012 IEEE International Conference on Management of Innovation & Technology (ICMIT); Bali, Indonesia. 11–13 June 2012; pp. 378–383. [ DOI ] [ Google Scholar ]
• 32. Georgiou O., Tsapatsoulis N. Improving the Scalability of Recommender Systems by Clustering Using Genetic Algorithms; Proceedings of the International Conference on Artificial Neural Networks; Sanya, China. 23–24 October 2010; pp. 442–449. [ DOI ] [ Google Scholar ]
• 33. Ge Y., Zhao S., Zhou H., Pei C., Sun F., Ou W., Zhang Y. Understanding echo chambers in e-commerce recommender systems; Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval; Xi’an, China. 25–30 July 2020. [ Google Scholar ]
• 34. Wang Q., Ma Y., Zhao K., Tian Y. A Comprehensive Survey of Loss Functions in Machine Learning. Ann. Data Sci. 2020;9:187–212. doi: 10.1007/s40745-020-00253-5. [ DOI ] [ Google Scholar ]
• 35. Farashah M.V., Etebarian A., Azmi R., Dastjerdi R.E. A hybrid recommender system based-on link prediction for movie baskets analysis. J. Big Data. 2021;8:32. doi: 10.1186/s40537-021-00422-0. [ DOI ] [ Google Scholar ]
• 36. Ortega F., Mayor J., López-Fernández D., Lara-Cabrera R. CF4J 2.0: Adapting Collaborative Filtering for Java to new challenges of collaborative filtering based recommender systems. Knowl. Based Syst. 2020;215:106629. doi: 10.1016/j.knosys.2020.106629. [ DOI ] [ Google Scholar ]
• 37. Al-Bakri N.F., Hashim S.H. Reducing Data Sparsity in Recommender Systems. Al-Nahrain J. Sci. 2018;21:138–147. doi: 10.22401/JNUS.21.2.20. [ DOI ] [ Google Scholar ]
• 38. Al-Bakri N.F., Hashim S.H. Collaborative Filtering Recommendation Model Based on k-means Clustering. Al-Nahrain J. Sci. 2019;22:74–79. doi: 10.22401/ANJS.22.1.10. [ DOI ] [ Google Scholar ]
• 39. Ahuja R., Solanki A., Nayyar A. Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor; Proceedings of the 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence); Noida, India. 10–11 January 2019; pp. 263–268. [ DOI ] [ Google Scholar ]
• 40. Balabanović M., Shoham Y. Fab: Content-based, collaborative recommendation. Commun. ACM. 1997;40:66–72. doi: 10.1145/245108.245124. [ DOI ] [ Google Scholar ]
• 41. Belavagi M.C., Muniyal B. Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. Procedia Comput. Sci. 2016;89:117–123. doi: 10.1016/j.procs.2016.06.016. [ DOI ] [ Google Scholar ]
• 42. Markos A.I., Vozalis M.G., Margaritis K.G. An Optimal Scaling Approach to Collaborative Filtering Using Categorical Principal Component Analysis and Neighborhood Formation; Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations; Hersonissos, Greece. 25–27 June 2010; pp. 22–29. [ DOI ] [ Google Scholar ]
• 43. Kumar A., Sharma A. Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) Springer; Berlin/Heidelberg, Germany: 2013. Alleviating sparsity and scalability issues in collaborative filtering based recommender systems. [ Google Scholar ]
• 44. Park D.-H., Kim H.-K., Choi I.-Y., Kim J.K. A Literature Review and Classification of Recommender Systems on Academic Journals. J. Intell. Inf. Syst. 2011;17:139–152. [ Google Scholar ]
• 45. Quijano-Sanchez L., Recio-Garcia J.A., Diaz-Agudo B., Jimenez-Diaz G. Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. 2013;4:1–30. doi: 10.1145/2414425.2414433. [ DOI ] [ Google Scholar ]
• 46. Himel M.T., Uddin M.N., Hossain M.A., Jang Y.M. Weight based movie recommendation system using K-means algorithm; Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC); Jeju Island, Korea. 18–20 October 2017. [ Google Scholar ]
• 47. Kourtit K., Nijkamp P., Arribas D. Smart cities in perspective–a comparative European study by means of self-organizing maps. Innov. Eur. J. Soc. Sci. Res. 2012;25:229–246. doi: 10.1080/13511610.2012.660330. [ DOI ] [ Google Scholar ]
• 48. Singh A., Thakur N., Sharma A. A review of supervised machine learning algorithms; Proceedings of the 3rd International Conference on Computing for Sustainable Global Development (INDIACom); New Delhi, India. 16–18 March 2016. [ Google Scholar ]
• 49. Sovilj D., Raiko T., Oja E. Extending Self-Organizing Maps with uncertainty information of probabilistic PCA; Proceedings of the International Joint Conference on Neural Networks (IJCNN); Barcelona, Spain. 18–23 July 2010; pp. 1–7. [ DOI ] [ Google Scholar ]
• 50. Madadipouya K., Chelliah S. A literature review on recommender systems algorithms, techniques and evaluations. Broad Res. Artif. Intell. Neurosci. 2017;8:109–124. [ Google Scholar ]
• 51. Berus L., Klancnik S., Brezocnik M., Ficko M. Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks. Sensors. 2018;19:16. doi: 10.3390/s19010016. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 52. Shi Y., Larson M., Hanjalic A. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Comput. Surv. 2014;47:1–45. doi: 10.1145/2556270. [ DOI ] [ Google Scholar ]
• 53. Karaboga D. Artificial bee colony algorithm. Scholarpedia. 2010;5:6915. doi: 10.4249/scholarpedia.6915. [ DOI ] [ Google Scholar ]
• 54. Zhang Z.-K., Liu C., Zhang Y.-C., Zhou T. Solving the cold-start problem in recommender systems with social tags. Eur. Lett. 2010;92:28002. doi: 10.1209/0295-5075/92/28002. [ DOI ] [ Google Scholar ]
• 55. Kumar M.S., Prabhu J. Hybrid model for movie recommendation system using fireflies and fuzzy c-means. Int. J. Web Portals. 2019;11:1–13. doi: 10.4018/IJWP.2019070101. [ DOI ] [ Google Scholar ]
• 56. Shanmugasundar G., Fegade V., Mahdal M., Kalita K. Optimization of Variable Stiffness Joint in Robot Manipulator Using a Novel NSWOA-MARCOS Approach. Processes. 2022;10:1074. doi: 10.3390/pr10061074. [ DOI ] [ Google Scholar ]
• 57. Kalita K., Ghadai R.K. Optimization of Plasma Enhanced Chemical Vapor Deposition Process Parameters for Hardness improvement of Diamond Like Carbon Coatings. Sci. Iran. 2022 doi: 10.24200/SCI.2022.56869.4952. [ DOI ] [ Google Scholar ]
• 58. Kalita K., Ghadai R.K., Chakraborty S. Parametric optimization of CVD process for DLC Thin film coatings: A comparative analysis. Sādhanā. 2022;47:57. doi: 10.1007/s12046-022-01842-1. [ DOI ] [ Google Scholar ]
• 59. Kalita K., Ghadai R.K., Bansod A. Sensitivity Analysis of GFRP Composite Drilling Parameters and Genetic Algorithm-Based Optimisation. Int. J. Appl. Metaheuristic Comput. 2022;13:1–17. doi: 10.4018/IJAMC.290539. [ DOI ] [ Google Scholar ]
• 60. Shankar R., Ganesh N., Čep R., Narayanan R.C., Pal S., Kalita K. Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization. Processes. 2022;10:616. doi: 10.3390/pr10030616. [ DOI ] [ Google Scholar ]
• 61. Rajendran S., Ganesh N., Čep R., Narayanan R.C., Pal S., Kalita K. A Conceptual Comparison of Six Nature-Inspired Metaheuristic Algorithms in Process Optimization. Processes. 2022;10:197. doi: 10.3390/pr10020197. [ DOI ] [ Google Scholar ]
• 62. Kalita K., Pal S., Haldar S., Chakraborty S. A Hybrid TOPSIS-PR-GWO Approach for Multi-objective Process Parameter Optimization. Process Integr. Optim. Sustain. 2022:1–16. doi: 10.1007/s41660-022-00256-0. [ DOI ] [ Google Scholar ]
• 63. Joshi M., Ghadai R.K., Madhu S., Kalita K., Gao X.-Z. Comparison of NSGA-II, MOALO and MODA for Multi-Objective Optimization of Micro-Machining Processes. Materials. 2021;14:5109. doi: 10.3390/ma14175109. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 64. Pal S., Kalita K., Haldar S. Genetic Algorithm-Based Fundamental Frequency Optimization of Laminated Composite Shells Carrying Distributed Mass. J. Inst. Eng. Ser. C. 2022;103:389–401. doi: 10.1007/s40032-021-00801-9. [ DOI ] [ Google Scholar ]
• 65. Kalita K., Dey P., Joshi M., Haldar S. A response surface modelling approach for multi-objective optimization of composite plates. Steel Compos. Struct. 2019;32:455–466. [ Google Scholar ]
• 66. Abdel-Basset M., Mohamed R., Elkomy O.M., Abouhawwash M. Recent metaheuristic algorithms with genetic operators for high-dimensional knapsack instances: A comparative study. Comput. Ind. Eng. 2022;166:107974. doi: 10.1016/j.cie.2022.107974. [ DOI ] [ Google Scholar ]
• 67. Joshi M., Kalita K., Jangir P., Ahmadianfar I., Chakraborty S. A Conceptual Comparison of Dragonfly Algorithm Variants for CEC-2021 Global Optimization Problems. Arab. J. Sci. Eng. 2022:1–31. doi: 10.1007/s13369-022-06880-9. [ DOI ] [ Google Scholar ]
• 68. Bacanin N., Zivkovic M., Bezdan T., Venkatachalam K., Abouhawwash M. Modified firefly algorithm for workflow scheduling in cloud-edge environment. Neural Comput. Appl. 2022;34:9043–9068. doi: 10.1007/s00521-022-06925-y. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 69. Abdel-Basset M., Mohamed R., Abouhawwash M. A new fusion of whale optimizer algorithm with Kapur’s entropy for multi-threshold image segmentation: Analysis and validations. Artif. Intell. Rev. 2022:1–71. doi: 10.1007/s10462-022-10157-w. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 70. Abdel-Basset M., Mohamed R., Abouhawwash M. Hybrid marine predators algorithm for image segmentation: Analysis and validations. Artif. Intell. Rev. 2021;55:3315–3367. doi: 10.1007/s10462-021-10086-0. [ DOI ] [ PMC free article ] [ PubMed ] [ Google Scholar ]
• 71. Abdel-Basset M., Mohamed R., AbdelAziz N.M., Abouhawwash M. HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation. Expert Syst. Appl. 2021;190:116145. doi: 10.1016/j.eswa.2021.116145. [ DOI ] [ Google Scholar ]
• 72. Kant V., Bharadwaj K.K. Proceedings of the Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) Springer; New Delhi, India: 2013. A user-oriented content based recommender system based on reclusive methods and interactive genetic algorithm. [ Google Scholar ]
• 73. Koosha H.R., Ghorbani Z., Nikfetrat R. A Clustering-Classification Recommender System based on Firefly Algorithm. J. AI Data Min. 2022;10:103–116. [ Google Scholar ]
• 74. Karaboga D., Akay B. A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 2009;214:108–132. doi: 10.1016/j.amc.2009.03.090. [ DOI ] [ Google Scholar ]
• 75. Chen M., Liu P. Performance evaluation of recommender systems. Int. J. Perform. Eng. 2017;13:1246. doi: 10.23940/ijpe.17.08.p7.12461256. [ DOI ] [ Google Scholar ]
• 76. Guy I., Zwerdling N., Ronen I., Carmel D., Uziel E. Social media recommendation based on people and tags; Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval; Geneva, Switzerland. 19–23 July 2010. [ Google Scholar ]
• 77. Zhu G., Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 2010;217:3166–3173. doi: 10.1016/j.amc.2010.08.049. [ DOI ] [ Google Scholar ]
• 78. Katarya R. Movie recommender system with metaheuristic artificial bee. Neural Comput. Appl. 2018;30:1983–1990. doi: 10.1007/s00521-017-3338-4. [ DOI ] [ Google Scholar ]
• 79. Bolaji A.L., Khader A.T., Al-Betar M.A., Awadallah M.A. Artificial bee colony algorithm, its variants and applications: A survey. J. Theor. Appl. Inf. Technol. 2013;47:434–459. [ Google Scholar ]
• 80. Haghgu Z., Hasheminejad S.M.H., Azmi R. A Novel Data Filtering for a Modified Cuckoo Search Based Movie Recommender; Proceedings of the 7th International Conference on Web Research (ICWR); Tehran, Iran. 19–20 May 2021; pp. 243–247. [ DOI ] [ Google Scholar ]
• 81. Katarya R., Verma O.P. Recommender system with grey wolf optimizer and FCM. Neural Comput. Appl. 2016;30:1679–1687. doi: 10.1007/s00521-016-2817-3. [ DOI ] [ Google Scholar ]
• 82. Sivaramakrishnan N., Subramaniyaswamy V., Ravi L., Vijayakumar V., Gao X.-Z., Sri S.R. An effective user clustering-based collaborative filtering recommender system with grey wolf optimization. Int. J. Bio Inspired Comput. 2020;16:44–55. doi: 10.1504/IJBIC.2020.108999. [ DOI ] [ Google Scholar ]
• 83. Papneja S., Sharma K., Khilwani N. Movie Recommendation to Friends Using Whale Optimization Algorithm, Recent Advances in Computer Science and Communications. Recent Pat. Comput. Sci. 2021;14:1470–1475. doi: 10.2174/2213275912666190823104600. [ DOI ] [ Google Scholar ]
• 84. Tripathi A.K., Mittal H., Saxena P., Gupta S. A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm. Complex Intell. Syst. 2020;7:297–309. doi: 10.1007/s40747-020-00200-0. [ DOI ] [ Google Scholar ]
• 85. Zhang Y., Wang S., Ji G. A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications. Math. Probl. Eng. 2015;2015:931256. doi: 10.1155/2015/931256. [ DOI ] [ Google Scholar ]
• 86. Singh P.K., Pramanik P.D., Choudhury P. New Age Analytics: Transforming the Internet through Machine Learning, IoT, and Trust Modeling. Apple Academic Press; Burlington, ON, Canada: 2020. Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends; pp. 183–215. [ Google Scholar ]
• 87. Karaboga D., Ozturk C. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 2011;11:652–657. doi: 10.1016/j.asoc.2009.12.025. [ DOI ] [ Google Scholar ]
• 88. Navin J.R.M., Pankaja R. Performance analysis of text classification algorithms using confusion matrix. Int. J. Eng. Tech. Res. 2016;6:75–78. [ Google Scholar ]
• 89. Marom N.D., Rokach L., Shmilovici A. Using the confusion matrix for improving ensemble classifiers; Proceedings of the IEEE 26th Convention of Electrical and Electronics Engineers; Eilat, Israel. 17–20 November 2010. [ Google Scholar ]
• 90. Mahmoud D.S., John R.I. Enhanced content-based filtering algorithm using Artificial Bee Colony optimisation; Proceedings of the SAI Intelligent Systems Conference (IntelliSys); London, UK. 10–11 November 2015; pp. 155–163. [ DOI ] [ Google Scholar ]
• 91. Nair A.M., Preethi N. IoT and Analytics for Sensor Networks. Springer; Berlin/Heidelberg, Germany: 2021. A Pragmatic Study on Movie Recommender Systems Using Hybrid Collaborative Filtering; pp. 489–494. [ DOI ] [ Google Scholar ]
• 92. Mathieu M., Couprie C., LeCun Y. Deep multi-scale video prediction beyond mean square error. arXiv. 20151511.05440 [ Google Scholar ]
• 93. Yadav N., Mundotiya R.K., Singh A.K., Pal S. Diversity in Recommendation System: A Cluster Based Approach; Proceedings of the International Conference on Hybrid Intelligent Systems; Online. 14–16 December 2020; pp. 113–122. [ DOI ] [ Google Scholar ]
• 94. Ramzan B., Bajwa I.S., Jamil N., Amin R.U., Ramzan S., Mirza F., Sarwar N. An Intelligent Data Analysis for Recommendation Systems Using Machine Learning. Sci. Program. 2019;2019:5941096. doi: 10.1155/2019/5941096. [ DOI ] [ Google Scholar ]
• 95. Dhankhad S., Mohammed E., Far B. Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study; Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration (IRI); Salt Lake City, UT, USA. 6–10 July 2018; pp. 122–125. [ DOI ] [ Google Scholar ]
• 96. Santra A.K., Christy C.J. Genetic algorithm and confusion matrix for document clustering. Int. J. Comput. Sci. Issues. 2012;9:322. [ Google Scholar ]
• 97. Lam X.N., Vu T., Le T.D., Duong A.D. Addressing cold-start problem in recommendation systems; Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication; Suwon, Korea. 31 January–1 February 2008. [ Google Scholar ]
• 98. Liang T., Wu S., Cao D. Emerging Computation and Information teChnologies for Education. Springer; Berlin/Heidelberg, Germany: 2012. Improved Collaborative Filtering Method Applied in Movie Recommender System; pp. 427–432. [ DOI ] [ Google Scholar ]
• 99. Mekouar L., Iraqi Y., Damaj I., Naous T. A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy. Comput. Commun. 2022;187:1–19. doi: 10.1016/j.comcom.2022.01.020. [ DOI ] [ Google Scholar ]
• 100. Schedl M., Zamani H., Chen C.-W., Deldjoo Y., Elahi M. Current challenges and visions in music recommender systems research. Int. J. Multimedia Inf. Retr. 2018;7:95–116. doi: 10.1007/s13735-018-0154-2. [ DOI ] [ Google Scholar ]
• 101. Das D., Chidananda H.T., Sahoo L. Progress in Computing, Analytics and Networking. Springer; Berlin/Heidelberg, Germany: 2018. Personalized Movie Recommendation System Using Twitter Data; pp. 339–347. [ DOI ] [ Google Scholar ]
• 102. Sarwar B.M. Sparsity, Scalability, and Distribution in Recommender Systems. University of Minnesota; Minneapolis, MN, USA: 2001. [ Google Scholar ]
• 103. Ponnam L.T., Punyasamudram S.D., Nallagulla S.N., Yellamati S. Movie recommender system using item based collaborative filtering technique; Proceedings of the International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS); Pudukkottai, India. 24–26 February 2016. [ Google Scholar ]
• 104. Sethi D., Singhal A. Comparative analysis of a recommender system based on ant colony optimization and artificial bee colony optimization algorithms; Proceedings of the 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT); Delhi, India. 3–5 July 2017; pp. 1–4. [ DOI ] [ Google Scholar ]
• 105. Zhou T., Chen L., Shen J. Movie recommendation system employing the user-based cf in cloud computing; Proceedings of the 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC); Guangzhou, China. 21–24 July 2017. [ Google Scholar ]
• 106. Shukla S., Naganna S. A review on K-means data clustering approach. Int. J. Inf. Comput. Technol. 2014;4:1847–1860. [ Google Scholar ]
• 107. Himeur Y., Sayed A., Alsalemi A., Bensaali F., Amira A., Varlamis I., Eirinaki M., Sardianos C., Dimitrakopoulos G. Blockchain-based recommender systems: Applications, challenges and future opportunities. Comput. Sci. Rev. 2022;43:100439. doi: 10.1016/j.cosrev.2021.100439. [ DOI ] [ Google Scholar ]
• 108. Singh M. Scalability and sparsity issues in recommender datasets: A survey. Knowl. Inf. Syst. 2018;62:1–43. doi: 10.1007/s10115-018-1254-2. [ DOI ] [ Google Scholar ]
• 109. Vellaichamy V., Kalimuthu V. Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization. Int. J. Intell. Eng. Syst. 2017;10:38–47. doi: 10.22266/ijies2017.1031.05. [ DOI ] [ Google Scholar ]

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A comprehensive analysis on movie recommendation system employing collaborative filtering

• Published: 08 June 2021
• Volume 80 , pages 28647–28672, ( 2021 )

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• Urvish Thakker 1 ,
• Ruhi Patel 1 &
• Manan Shah 2  

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Collaborative Filtering (CF) is one of the most extensively used technologies for Recommender Systems (RS), it shows an improved intelligent searching mechanism for recommending personalized items. It effectively makes use of the information retained by the application to find similarities between the sections of the application. Apart from RS, other applications of CF making use of the sensing and monitoring of data are environmental sensing, mineral study, financial services, marketing, and many more. Different industries like Tourism, Television, E-Learning, etc. make use of this technology, software such as Customer Relationship Management also make use of this technology. This paper discusses the prowess CF algorithm and its applications for Movie Recommendation System (MRS). It gives a brief overview of collaborative filtering consisting of two major approaches: user-based approach and Item-based approaches. Further, in model-based filtering methodology, it is discussed how machine learning algorithms can be implemented for movie recommendation purposes and also to predict the ratings of the unrated movies and bifurcate or sort movies as per the user preference. Followed by, it throws some light on the methodologies used in the late past and some of the basic approaches that are taken into consideration to incorporate it into MSR. Additionally, this paper anatomized many of the recent past studies in depth to draw out the essence of the researches and studies, its crucial steps, results, future scope and methodologies, followed and suggested by multiple researchers. Finally, we have discussed various challenges in MRS and probable future developments in this field. It is to be noted that various challenges in the field of CF recommendation systems like cold start, data sparsity, scalability issues, etc. were raised and many approaches tried to tackle these challenges in innovative and novel ways. Conclusively CF algorithm is a highly efficacious technique for the application of MRS and its integration with other techniques will lead students, researchers and enthusiasts to more cogent approaches for MRS.

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Movie Recommendation System

A Survey on Personalized Movie Recommendation System Using Machine Learning

Movie Recommendation Using Content-Based and Collaborative Filtering

Data availability.

All relevant data and material are presented in the main paper.

Abbreviations

Explanation

Collaborative Filtering.

Recommender Systems.

Movie Recommendation System.

Markovian Factorization of Matrix Process.

Singular Value decomposition.

Root Mean Squared Error.

Mean Absolute Error.

Particle Swarm Optimization.

Fuzzy c-means.

Alternating Least Squares.

Genetic Algorithm.

Application Programming Interfaces.

Effective Missing Data Prediction.

Pearson Correlation Coefficient.

Mean Squared Error.

Pure Content Based.

Hellinger Coefficient Based Collaborative Filtering.

Standard Deviation.

Mean Average Precision.

Aesthetic Visual Features.

Audio Information.

Visual Information.

Editorial Metadata.

Combination of correlation-based item similarity approach and Similarity calculation based on the genre and director movies.

Modified Collaborative Filtering Algorithm.

User-Item Interaction Records.

Number of neighbours.

K-Nearest Neighbour.

Singular Value Decomposition.

True Positive Ratio.

False Positive Ratio.

Aggarwal CC, Aggarwal CC (2016) Content-based recommender systems. Recomm Syst 139–166. https://doi.org/10.1007/978-3-319-29659-3_4

Ahn HJ A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. https://doi.org/10.1016/j.ins.2007.07.024

Al-Bashiri H, Abdulgabber MA, Romli A, Kahtan H (2018) An improved memory-based collaborative filtering method based on the TOPSIS technique PLoS One 13:. https://doi.org/10.1371/journal.pone.0204434

Alhijawi B (2019) Improving collaborative filtering recommender system results using optimization technique. ACM Int Conf proceeding Ser 183–187. https://doi.org/10.1145/3369114.3369126

Aljunid MF, Manjaiah DH (2019) Data management, analytics and innovation, advances in intelligent systems and Computing. Springer, Singapore

Google Scholar  

Aljunid MF, Manjaiah DH (2019) Movie recommender system based on collaborative filtering using apache spark. Springer, Singapore

Book   Google Scholar  

Avery C (1997) Recom System 40:88–89

Aygün S, Okyay S (2015) Improving the Pearson similarity equation for recommender systems by age parameter. 2015 IEEE 3rd Workshop on Advances in Information. Electronic and Electrical Engineering (AIEEE) 2015:1–6. https://doi.org/10.1109/AIEEE.2015.7367282

Banweer K, Graham A, Ripberger J, Cesare N, Nsoesie E, Grant C (2018) Multi-stage collaborative filtering for tweet geolocation. In: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Recommendations for Location-based Services and Social Networks (LocalRec'18), vol 4. Association for Computing Machinery, New York, NY, USA, pp 1–4. https://doi.org/10.1145/3282825.3282831

Barla M (2011) Towards social-based user modeling and personalization. Inf Sci Technol Bull ACM Slovakia 3:52–60

Beel J, Gipp B, Langer S, Breitinger C (2016) Research-paper recommender systems: a literature survey. Int J Digit Libr 17:305–338. https://doi.org/10.1007/s00799-015-0156-0

Article   Google Scholar  

Bellogín A, Parapar J Using Graph Partitioning Techniques for Neighbour Selection in User-Based Collaborative Filtering

Billsus D, Billsus D, Pazzani MJ, Pazzani MJ (1998) Learning collaborative information filters. Proc Fifteenth Int Conf Mach Learn 54:47

MATH   Google Scholar  

Bobadilla J, Ortega F, Hernando A (2012) A collaborative filtering similarity measure based on singularities. Inf Process Manag 48:204–217. https://doi.org/10.1016/j.ipm.2011.03.007

Breese JS, Heckerman D, Kadie C (2013) Empirical analysis of predictive algorithms for collaborative filtering. 43–52

Cami BR, Hassanpour H, Mashayekhi H (2018) A content-based movie recommender system based on temporal user preferences. Proc - 3rd Iran Conf Signal Process Intell Syst ICSPIS 2017 2017-Decem:121–125. https://doi.org/10.1109/ICSPIS.2017.8311601

Campos PG, Díez F, Cantador I (2014) Time-aware recommender systems: a comprehensive survey and analysis of existing evaluation protocols. User Model User-adapt Interact 24:67–119. https://doi.org/10.1007/s11257-012-9136-x

Chen YC, Hui L, Thaipisutikul T (2020) A collaborative filtering recommendation system with dynamic time decay. J Supercomput 77:244–262. https://doi.org/10.1007/s11227-020-03266-2

Chen VX, Tang TY (2019) Incorporating singular value decomposition in user-based collaborative filtering technique for a movie recommendation system: a comparative study. In: ACM international conference proceeding series. Association for Computing Machinery, New York, pp 12–15

Chen MH, Teng CH, Chang PC (2015) Applying artificial immune systems to collaborative filtering for movie recommendation. Adv Eng Inform 29:830–839. https://doi.org/10.1016/j.aei.2015.04.005

Chen HW, Wu YL, Hor MK, Tang CY (2017) Fully content-based movie recommender system with feature extraction using neural network. Proc 2017 Int Conf Mach learn Cybern ICMLC 2017 2:504–509. https://doi.org/10.1109/ICMLC.2017.8108968

Cho J, Roy S (2004) Impact of search engines on page popularity. Thirteen Int world wide web Conf proceedings, WWW2004 20–29. https://doi.org/10.1145/988672.988676

Christakou C, Stafylopatis A (2007) A hybrid movie recommender system based on neural networks 16:771–792

Claypool M, Gokhale A, Miranda T, et al (1999) Combing content-based and collaborative filters in an online newspaper

Cui G, Luo J, Wang X (2018) Personalized travel route recommendation using collaborative filtering based on GPS trajectories. Int J Digit Earth 11:284–307. https://doi.org/10.1080/17538947.2017.1326535

Das D, Chidananda HT, Sahoo L (2018) Personalized movie recommendation system using twitter data. Springer, Singapore, pp 339–347

Deldjoo Y, Dacrema MF, Constantin MG, Eghbal-zadeh H, Cereda S, Schedl M, Ionescu B, Cremonesi P (2019) Movie genome: alleviating new item cold start in movie recommendation. User Model User-adapt Interact 29:291–343. https://doi.org/10.1007/s11257-019-09221-y

Devooght R, Bersini H (2016) Collaborative filtering with recurrent neural networks

Do MPT, Nguyen DV, Nguyen L (2010) Model-based approach for collaborative filtering. In: 6 th International Conference on Information Technology for Education, pp 217–228

Dou Y, Yang H, Deng X (2017) A Survey of Collaborative Filtering Algorithms for Social Recommender Systems. Proc - 2016 12th Int Conf Semant Knowl grids, SKG 2016 40–46. https://doi.org/10.1109/SKG.2016.014

Dwicahya I, Rosa PH, Nugroho R (2019) Movie recommender system comparison of user-based and item-based collaborative filtering systems. https://doi.org/10.4108/eai.19-10-2018.2282541

Fernández-Tobías I, Braunhofer M, Elahi M, Ricci F, Cantador I (2016) Alleviating the new user problem in collaborative filtering by exploiting personality information. User Model User-adapt Interact 26:221–255. https://doi.org/10.1007/s11257-016-9172-z

Ferwerda B, Schedl M (2016) Personality-based user modeling for music recommender systems. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 254–257). Springer, Cham. https://doi.org/10.1007/978-3-319-46131-1_29

Gandhi S, Gandhi M (2018) Hybrid recommendation system with collaborative filtering and association rule mining using big data. 2018 3rd Int Conf Converg Technol I2CT 2018 1–5. https://doi.org/10.1109/I2CT.2018.8529683

Gao F, Xing C, Du X, Wang S (2007) Personalized service system based on hybrid filtering for digital library. Tsinghua Sci Technol 12:1–8. https://doi.org/10.1016/S1007-0214(07)70001-9

Geetha G, Safa M, Fancy C, Saranya D (2018, April) A hybrid approach using collaborative filtering and content based filtering for recommender system. In Journal of Physics: Conference Series (Vol. 1000, No. 1, p. 012101). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1000/1/012101/meta

Ghazi MR, Gangodkar D (2015) Hadoop, mapreduce and HDFS: a developers perspective. Procedia Comput Sci 48:45–50. https://doi.org/10.1016/j.procs.2015.04.108

Gomez-Uribe CA, Hunt N (2016) The Netflix recommender system: algorithms, business value, and innovation. ACM Trans Manage Inf Syst 6(4):Article 13 (January 2016), 19 pages. https://doi.org/10.1145/2843948

Gong S (2010) A collaborative filtering recommendation algorithm based on user clustering and item clustering. J Softw 5:745–752. https://doi.org/10.4304/jsw.5.7.745-752

Gorbunov RD, Rauterberg M, Barakova EI (2019) A cognitive model of social preferences in group interactions. Integrated Computer-Aided Engineering 26(2):185–196. https://doi.org/10.3233/ICA-180590

Grčar M, Fortuna B, Mladenič D, Grobelnik M (2006) kNN versus SVM in the collaborative filtering framework. Data Sci Classif:251–260. https://doi.org/10.1007/3-540-34416-0_27

Gurcan F, Birturk AA (2015) A hybrid movie recommender using dynamic fuzzy clustering. Lect Notes Electr Eng 363:159–169. https://doi.org/10.1007/978-3-319-22635-4_14

Hasan M, Tasdikul Hasan M, Selim Reza M, et al (2019) A comprehensive collaborative filtering approach using autoencoder in recommender system. ACM Int Conf proceeding Ser 185–189. https://doi.org/10.1145/3330482.3330518

He X, Liao L, Zhang H, et al (2017) Neural collaborative filtering. 26th Int world wide web Conf WWW 2017 173–182. https://doi.org/10.1145/3038912.3052569

Herlocker JL, Konstan JA, Riedl J (2000) Explaining collaborative filtering recommendations. Proc ACM Conf Comput Support Coop Work:241–250. https://doi.org/10.1145/358916.358995

Hill W, Stead L, Rosenstein M, Furnas G (1995) Recommending and evaluating choices in a virtual community of use. Conf Hum Factors Comput Syst - Proc 1:194–201. https://doi.org/10.1145/223904.223929

Himel MT, Uddin MN, Hossain MA, Jang YM (2017) Weight based movie recommendation system using K-means algorithm. In: 2017 international conference on information and communication technology convergence (ICTC). IEEE, pp 1302–1306

Hu B, Li Z, Chao W (2012) Data sparsity: A key disadvantage of user-based collaborative filtering? Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 7235 LNCS:602–609. https://doi.org/10.1007/978-3-642-29253-8_55

Hu Y, Xiong F, Lu D, Wang X, Xiong X, Chen H (2020a) Movie collaborative filtering with multiplex implicit feedbacks. Neurocomputing 398:485–494. https://doi.org/10.1016/j.neucom.2019.03.098

Lim KH, Chan J, Leckie C, Karunasekera S (2015a) Personalized tour recommendation based on user interests and points of interest visit durations. In: Twenty-Fourth International Joint Conference on Artificial Intelligence, pp 1–7

Jafar O (n.d.) Emerging RS-P of IC on, 2013 undefined A comparative study of hard and fuzzy data clustering algorithms with cluster validity indices

Jain KN, Kumar V, Kumar P, Choudhury T (2018) Movie recommendation system: hybrid information filtering system. Adv Intell Syst Comput 673:677–686. https://doi.org/10.1007/978-981-10-7245-1_66

Jiang J, Lu J, Zhang G, Long G (2011) Scaling-up item-based collaborative filtering recommendation algorithm based on Hadoop. Proc - 2011 IEEE world Congr Serv Serv 2011 490–497. https://doi.org/10.1109/SERVICES.2011.66

Jones MT (2013) Recommender systems , Part 1 : Introduction to approaches and algorithms Learn about the concepts that underlie web recommendation engines. 1–8

Kannan R, Ghinea G, Swaminathan S (2015) What do you wish to see? A summarization system for movies based on user preferences. Inf Process Manag 51:286–305. https://doi.org/10.1016/j.ipm.2014.12.001

Katarya R, Verma OP (2016) A collaborative recommender system enhanced with particle swarm optimization technique. Multimed Tools Appl 75:9225–9239. https://doi.org/10.1007/s11042-016-3481-4

Katarya R, Verma OP (2017) An effective collaborative movie recommender system with cuckoo search. Egypt Inform J 18:105–112. https://doi.org/10.1016/j.eij.2016.10.002

Kermarrec AM, Leroy V, Moin A, Thraves C (2010a) Application of random walks to decentralized recommender systems. In: Lu C, Masuzawa T, Mosbah M (eds) Principles of distributed systems. OPODIS 2010. Lecture Notes in Computer Science, vol 6490. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17653-1_4

Kharita MK, Kumar A, Singh P (2018a) Item-based collaborative filtering in movie recommendation in real time. In: 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), pp 340–342. https://doi.org/10.1109/ICSCCC.2018.8703362

Konstan JA, Miller BN, Maltz D, Herlocker JL, Gordon LR, Riedl J (1997a) Grouplens: Applying collaborative filtering to usenet news. Communications of the ACM 40(3):77–87. https://doi.org/10.1145/245108.245126

Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy C-means. Meas J Int Meas Confed 91:134–139. https://doi.org/10.1016/j.measurement.2016.05.058

Koren Y, Bell R, Volinsky C (2009) COVER feature matrix techniques for. 30–37

Kunaver M, Tasic J, Kosir A, et al (2009) Personality based user similarity measure for a collaborative recommender system

Lee D, Hosanagar K (2014a) Impact of recommender systems on sales volume and diversity. In: Thirty Fifth International Conference on Information Systems, pp 1–15

Lekakos G, Caravelas P (2008a) A hybrid approach for movie recommendation. Multimed Tools Appl 36:55–70. https://doi.org/10.1007/s11042-006-0082-7

Leng Y, Liang C, Ding Y, et al Method of neighborhood formation in collaborative filtering

Li J, Xu W, Wan W, Sun J (2018) Movie recommendation based on bridging movie feature and user interest. J Comput Sci 26:128–134. https://doi.org/10.1016/j.jocs.2018.03.009

Liang T, Wu S, Cao D Applied in Movie Recommender System. 427–432

Lin C-H, Chi H (2020) A novel movie recommendation system based on collaborative filtering and neural networks. In: International Conference on Advanced Information Networking and Applications. Springer International Publishing, pp. 895–903

Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7:76–80. https://doi.org/10.1109/MIC.2003.1167344

Liu H, Kong X, Bai X, Wang W, Bekele TM, Xia F (2015) Context-based collaborative filtering for citation recommendation. IEEE Access 3:1695–1703. https://doi.org/10.1109/ACCESS.2015.2481320

Liu G, Wu X (2019) Using collaborative filtering algorithms combined with Doc2Vec for movie recommendation. Proc 2019 IEEE 3rd Inf Technol networking. Electron Autom Control Conf ITNEC 2019:1461–1464. https://doi.org/10.1109/ITNEC.2019.8729076

Lops P, Jannach D, Musto C, Bogers T, Koolen M (2019) Trends in content-based recommendation: preface to the special issue on recommender systems based on rich item descriptions. User Model User-adapt Interact 29:239–249. https://doi.org/10.1007/s11257-019-09231-w

Maheswari M, Geetha S, Selva Kumar S (2019) Adaptable and proficient Hellinger coefficient based collaborative filtering for recommendation system. Clust Comput 22:12325–12338. https://doi.org/10.1007/s10586-017-1616-7

Moshfeghi Y, Piwowarski B, Jose JM (2011) Handling data sparsity in collaborative filtering using emotion and semantic based features. In: SIGIR’11 - proceedings of the 34th international ACM SIGIR conference on Research and Development in information retrieval. Association for Computing Machinery, New York, pp 625–634

Mu R, Zeng X (2020) Auxiliary stacked denoising autoencoder based collaborative filtering recommendation. KSII Trans Internet Inf Syst 14:2310–2332. https://doi.org/10.3837/tiis.2020.06.001

Nguyen QN, Duong-Trung N, Le Ha DN et al (2020) Movie recommender systems made through tag interpolation. In: Proceedings of the 4th international conference on machine learning and soft computing. ACM, New York, pp 154–158

Chapter   Google Scholar  

Nilashi M, Bagherifard K, Ibrahim O et al (2013) Collaborative filtering recommender systems. Res J Appl Sci Eng Technol 5:4168–4182. https://doi.org/10.19026/rjaset.5.4644

Özbal G, Karaman H, Alpaslan FN (2011) A content-boosted collaborative filtering approach for movie recommendation based on local and global similarity and missing data prediction. The Computer Journal 54(9):1535–1546. https://doi.org/10.1093/comjnl/bxr001

Pan Y, He F, Yu H (2019) A novel enhanced collaborative autoencoder with knowledge distillation for top-N recommender systems. Neurocomputing 332:137–148. https://doi.org/10.1016/j.neucom.2018.12.025

Pan Y, He F, Yu H (2020) A correlative denoising autoencoder to model social influence for top-N recommender system. Front Comput Sci 14. https://doi.org/10.1007/s11704-019-8123-3

Pan Y, He F, Yu H (2020) Learning social representations with deep autoencoder for recommender system. World Wide Web 23:2259–2279. https://doi.org/10.1007/s11280-020-00793-z

Papadakis H, Michalakis N, Fragopoulou P, et al (2017) Movie SCoRe: Personalized movie recommendation on mobile devices ACM Int Conf Proceeding Ser Part F1325: https://doi.org/10.1145/3139367.3139383

Patra S, Ganguly B (2019) Improvising singular value decomposition by KNN for use in movie recommender systems. J Oper Strateg Plan 2:22–34. https://doi.org/10.1177/2516600x19848956

Pirasteh P, Jung JJ, Hwang D (2014) Item-based collaborative filtering with attribute correlation: A case study on movie recommendation. Lect Notes Comput Sci (including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 8398 LNAI:245–252. https://doi.org/10.1007/978-3-319-05458-2_26

Ponnam LT, Deepak Punyasamudram S, Nallagulla SN, Yellamati S (2016) Movie recommender system using item based collaborative filtering technique. 1st Int Conf Emerg trends Eng Technol Sci ICETETS 2016 - Proc 1–5. https://doi.org/10.1109/ICETETS.2016.7602983

Purnomo JE, Endah SN (2019) Rating prediction on movie recommendation system: collaborative filtering algorithm (CFA) vs. Dissymetrical percentage collaborative filtering algorithm (DSPCFA). ICICOS 2019 - 3rd Int Conf informatics Comput Sci Accel informatics Comput res smarter Soc era Ind 40, Proc 1–6. https://doi.org/10.1109/ICICoS48119.2019.8982385

Ren X, Dai Y, Ning D, Chen Y (2016) Course selection of students based on collaborative filtering. 594–597. https://doi.org/10.2991/emcs-16.2016.144

Resnick P, Iacovou N, Suchak M, et al (1994) GroupLens: an open architecture for collaborative filtering of netnews. Proc 1994 ACM Conf Comput support coop work CSCW 1994 175–186. https://doi.org/10.1145/192844.192905

Resnick P, Varian HR, Editors G (1997) Recommender Systems mmende tems. Commun ACM 40:56–58

Ricci F, Rokach L, Shapira B (2011) Recommender Systems Handbook

Rich E (1979) User modeling via stereotypes. Cogn Sci 3:329–354. https://doi.org/10.1016/S0364-0213(79)80012-9

Ruotsalo T, Haav K, Stoyanov A, Roche S, Fani E, Deliai R, Mäkelä E, Kauppinen T, Hyvönen E (2013) SMARTMUSEUM: a mobile recommender system for the web of data. J Web Semant 20:50–67. https://doi.org/10.1016/j.websem.2013.03.001

Sanchez JL, Ferradilla S, Martinez E, Bobadilla J (2008) Choice of metrics used in collaborative filtering and their impact on recommender systems. In: 2008 2nd IEEE International Conference on Digital Ecosystems and Technologies, pp 432–436. https://doi.org/10.1109/DEST.2008.4635147

Prateek S, Sadhwani Y, Arora P (2017) Movie recommender system. Search Engine Architecture, Spring 2017:1–5

Sarwar B, Karypis G, Konstan J, Riedl J (2001) Proceedings of the 10th international conference on World Wide Web, pp 285–295. https://doi.org/10.1145/371920.372071

Schafer BJ, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems - CollaborativeFilteringRecommenderSystems.Pdf. Lncs 4321:291–324

Schwartz B (2005) The paradox of choice. Why More is Less American Mania . When More is Not Enough. Finance 265

Shani G, Gunawardana A (2011) Recommender Systems Handbook

Shardanand U, Maes P (1995) Social information filtering: algorithms for automating “word of mouth”. Conf Hum Factors Comput Syst - Proc 1:210–217

Sheugh L, Alizadeh SH (2015) A note on Pearson correlation coefficient as a metric of similarity in recommender system. 2015 AI robot IRANOPEN 2015 - 5th Conf Artif Intell robot. https://doi.org/10.1109/RIOS.2015.7270736

Shi Y, Larson M, Hanjalic A (2014) Collaborative filtering beyond the user-item matrix: a survey of the state of the art and future challenges. ACM Comput Surv 47:1–45. https://doi.org/10.1145/2556270

Marko B, Shoham Y (1997) Fab: content-based, collaborative recommendation. Communications of the ACM 40(3):66–72. https://doi.org/10.1145/245108.245124

Shristi JAK, Mohanty SN (2018) A collaborative filtering approach for movies recommendation based on user clustering and item clustering. Springer, Singapore

Singh VK, Mukherjee M, Mehta GK (2011) Combining collaborative filtering and sentiment classification for improved movie recommendations. In: Sombattheera C, Agarwal A, Udgata SK, Lavangnananda K (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2011. Lecture Notes in Computer Science, vol 7080. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_4

Stakhiyevich P, Huang Z (2019) An experimental study of building user profiles for movie recommender 898 system. In: In: Proc - 21st IEEE Int Conf high perform Comput Commun 17th IEEE Int Conf Smart City 5 th 899 IEEE Int Conf data Sci Syst HPCC/SmartCity/DSS, vol 5, pp 2559–2565. https://doi.org/10.1109/HPCC/900SmartCity/DSS.2019.00358

Su X, Khoshgoftaar TM (2009) A survey of collaborative filtering techniques. Adv Artif Intell 2009:1–19. https://doi.org/10.1155/2009/421425

Subramaniyaswamy V, Logesh R, Chandrashekhar M, Challa A, Vijayakumar V (2017) A personalised movie recommendation system based on collaborative filtering. Int J High Perform Comput Netw 10:54–63. https://doi.org/10.1504/IJHPCN.2017.083199

Tao L, Jiao M, Dai Y, Gao C (2017) A multilayer collaborative filtering recommendation method in electricity market. Proc - 13th web Inf Syst Appl Conf WISA 2016 - conjunction with 1st Symp big data process anal BDPA 2016 1st work Inf Syst Secur ISS 2016 51–55. https://doi.org/10.1109/WISA.2016.20

Ungar LH, Foster DP (1998) Clustering methods for collaborative filtering. AAAI Workshop on Recommendation Systems 1:114–129

Uyangoda L, Ahangama S, Ranasinghe T (2018) User profile feature-based approach to address the cold start problem in collaborative filtering for personalized movie recommendation. In: 2018 13th Int Conf digit Inf Manag ICDIM, pp 24–28. https://doi.org/10.1109/ICDIM.2018.8847002

Uyangoda L, Ahangama S, Ranasinghe T User Profile Feature-Based Approach to Address the Cold Start Problem in Collaborative Filtering for Personalized Movie Recommendation

Vimala S V, Vivekanandan K (2019) A Kullback–Leibler divergence-based fuzzy C-means clustering for enhancing the potential of an movie recommendation system SN Appl Sci 1:. https://doi.org/10.1007/s42452-019-0708-9

Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408

Wang L, Meng X, Zhang Y (2011) A heuristic approach to social network-based and context-aware mobile services recommendation. J Converg Inf Technol 6:339–346. https://doi.org/10.4156/jcit.vol6.issue10.43

Wang J, Reinders MJT, De Vries AP (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion the brain in Duchenne muscular dystrophy view project VITALAS view project unifying user-based and item-based collaborative filtering approaches by similarity fusion. https://doi.org/10.1145/1148170.1148257

Wang H, Wang N, Yeung DY (2015) Collaborative deep learning for recommender systems. Proc ACM SIGKDD Int Conf Knowl Discov Data Min 2015-Augus:1235–1244. https://doi.org/10.1145/2783258.2783273

Wang Z, Yu X, Feng N, Wang Z (2014) An improved collaborative movie recommendation system using computational intelligence. J Vis Lang Comput 25:667–675. https://doi.org/10.1016/j.jvlc.2014.09.011

Weber I, Castillo C (2010) The demographics of web search. SIGIR 2010 Proc - 33rd Annu Int ACM SIGIR Conf res Dev Inf Retr 523–530. https://doi.org/10.1145/1835449.1835537

Wei J, He J, Chen K, Zhou Y, Tang Z (2017) Collaborative filtering and deep learning based recommendation system for cold start items. Elsevier. 69:29–39. https://doi.org/10.1016/j.eswa.2016.09.040,

Weng S-S, Lee C (2013) Integration of content-based approach and hybrid collaborative filtering for movie recommendation. Int Conf Bus Inf

Wu C-SM, Garg D, Bhandary U (2018) Movie recommendation system using collaborative filtering. In: 2018 IEEE 9th international conference on software engineering and service science (ICSESS). IEEE, pp 11–15

Xiao P, Shao L, Li X (2013) Improved collaborative filtering algorithm in the research and application of personalized movie recommendations. Proc - 2013 4th Int Conf Intell Syst des Eng Appl ISDEA 2013 349–352. https://doi.org/10.1109/ISDEA.2013.483

Yang C, Akimoto Y, Kim DW, Udell M (2019) OBoe: collaborative filtering for automl model selection. Proc ACM SIGKDD Int Conf Knowl Discov Data Min:1173–1183. https://doi.org/10.1145/3292500.3330909

Yang Z, Wu B, Zheng K, Wang X, Lei L (2016) A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access 4:3273–3287. https://doi.org/10.1109/ACCESS.2016.2573314

Yingyuan X, Pengqiang AI, Hsu C, et al (2015) II. RELATED WORK 2.1 content-based recommendation. 53–62

Yu L, Liu L, Li X (2005) A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in E-commerce. Expert Syst Appl 28:67–77. https://doi.org/10.1016/j.eswa.2004.08.013

Yu K, Schwaighofer A, Tresp V et al (2004) Probabilistic Memory-Based Collaborative Filtering 16:56–69

Zanitti M, Kosta S, Sørensen J (2018) A user-centric diversity by design recommender system for the movie application domain. Web Conf 2018 - companion world wide web Conf WWW 2018 1381–1389. https://doi.org/10.1145/3184558.3191580

Zargany E, Ahmadi A (2015) A new modular neural network approach for exchange rate prediction. Int J Electron Financ 8:97–123. https://doi.org/10.1504/IJEF.2015.070515

Zhao D, Xiu J, Zhengqiu Y, Liu C (2017) An improved user-based movie recommendation algorithm. 2016 2nd IEEE Int Conf Comput Commun ICCC 2016 - Proc 874–877. https://doi.org/10.1109/CompComm.2016.7924828

Zhou T, Chen L, Shen J (2017) Movie recommendation system employing the user-based CF in cloud computing. Proc - 2017 IEEE Int Conf Comput Sci Eng IEEE/IFIP Int Conf embed ubiquitous Comput CSE EUC 2017 2:46–50. https://doi.org/10.1109/CSE-EUC.2017.194

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Thakker, U., Patel, R. & Shah, M. A comprehensive analysis on movie recommendation system employing collaborative filtering. Multimed Tools Appl 80 , 28647–28672 (2021). https://doi.org/10.1007/s11042-021-10965-2

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Received : 26 June 2020

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