A systematic review of movie recommender systems
Abstract
Recommender systems are vital to everyone's information selection. Managing massive amounts of data is common with recommendation system technology. Annual film releases are rising, and currently films are released within months. With movie releases, apps like Netflix, Viu, Amazon Prime Video, Disney+, etc. have emerged. Thus, Movie Recommender Systems (MRS) are essential to simplify and improve user experience. This research gives a systematic literature review (SLR) of MRS's current condition. Our comprehensive review addresses recommendation algorithms, data processing, and evaluation approaches. In SLR MRS, content-based filtering, collaborative filtering, knowledge-based recommender systems, and hybrid approaches are employed. To achieve this, 66 high-quality studies were selected from 27,187 2019-2023 studies using strict quality criteria. The study found that most MRSs use content-based filtering and machine learning to deliver non-personalized movie suggestions in various domains. The review helps researchers choose MRS development strategies. This study can assist MRS development catch up to other recommendation systems by improving efficiency. The MRS investigation found accuracy, sparsity, scalability, cold start, and operating time issues. Future study will examine how temporal and demographic data affect movie recommendation system relevancy and customization.
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P. Mondal, P. Kapoor, S. Singh, S. Saha, J. P. Singh, and N. Onoe, “Task-Specific and Graph Convolutional Network based Multi-modal Movie Recommendation System in Indian Setting,” Procedia Comput. Sci., vol. 222, pp. 591–600, 2023, doi: 10.1016/j.procs.2023.08.197.
Z. Hu, S. M. Cai, J. Wang, and T. Zhou, “Collaborative recommendation model based on multi-modal multi-view attention network: Movie and literature cases,” Appl. Soft Comput., vol. 144, p. 110518, 2023, doi: 10.1016/j.asoc.2023.110518.
F. García-Sánchez, R. Colomo-Palacios, and R. Valencia-García, “A social-semantic recommender system for advertisements,” Inf. Process. Manag., vol. 57, no. 2, p. 102153, 2020, doi: 10.1016/j.ipm.2019.102153.
Y. Hu, F. Xiong, D. Lu, X. Wang, X. Xiong, and H. Chen, “Movie collaborative filtering with multiplex implicit feedbacks,” Neurocomputing, vol. 398, pp. 485–494, 2020, doi: 10.1016/j.neucom.2019.03.098.
Y. Gu, Z. Ding, S. Wang, and D. Yin, “Hierarchical user profiling for e-commerce recommender systems,” WSDM 2020 - Proc. 13th Int. Conf. Web Search Data Min., pp. 223–231, 2020, doi: 10.1145/3336191.3371827.
D. P. D. Rajendran and R. P. Sundarraj, “Using topic models with browsing history in hybrid collaborative filtering recommender system: Experiments with user ratings,” Int. J. Inf. Manag. Data Insights, vol. 1, no. 2, p. 100027, 2021, doi: 10.1016/j.jjimei.2021.100027.
G. Behera and N. Nain, “Collaborative Filtering with Temporal Features for Movie Recommendation System,” Procedia Comput. Sci., vol. 218, pp. 1366–1373, 2022, doi: 10.1016/j.procs.2023.01.115.
V. Jain and K. Bansal, “Novel Approach using user-based Similarity for Recommendation Systems,” Proc. 4th Int. Conf. Inven. Syst. Control. ICISC 2020, no. Icisc, pp. 522–526, 2020, doi: 10.1109/ICISC47916.2020.9171214.
B. Walek and V. Fojtik, “A hybrid recommender system for recommending relevant movies using an expert system,” Expert Syst. Appl., vol. 158, 2020, doi: 10.1016/j.eswa.2020.113452.
S. Putta and O. Kulkarni, Analytical Study of Content-Based and Collaborative Filtering Methods for Recommender Systems, vol. 936. Springer Nature Singapore, 2022.
B. Dai, X. Shen, J. Wang, and A. Qu, “Scalable Collaborative Ranking for Personalized Prediction,” J. Am. Stat. Assoc., vol. 116, no. 535, pp. 1215–1223, 2021, doi: 10.1080/01621459.2019.1691562.
M. S. Kim, B. Y. Lim, H. S. Shin, and H. Y. Kwon, “Historical credibility for movie reviews and its application to weakly supervised classification,” Inf. Sci. (Ny)., vol. 630, no. February, pp. 325–340, 2023, doi: 10.1016/j.ins.2023.01.138.
A. Breitfuss, K. Errou, A. Kurteva, and A. Fensel, “Representing emotions with knowledge graphs for movie recommendations,” Futur. Gener. Comput. Syst., vol. 125, pp. 715–725, 2021, doi: 10.1016/j.future.2021.06.001.
Y. J. Leng, Z. Y. Wu, Q. Lu, and S. Zhao, “Collaborative filtering based on multiple attribute decision making,” J. Exp. Theor. Artif. Intell., vol. 34, no. 3, pp. 387–397, 2022, doi: 10.1080/0952813X.2021.1882000.
P. M. T. Do and T. T. S. Nguyen, “Semantic-enhanced neural collaborative filtering models in recommender systems,” Knowledge-Based Syst., vol. 257, p. 109934, 2022, doi: 10.1016/j.knosys.2022.109934.
H. Lei, J. Liu, and Y. Yu, “Exemplar-based large-scale low-rank matrix decomposition for collaborative prediction,” Int. J. Comput. Math., vol. 100, no. 3, pp. 615–640, 2023, doi: 10.1080/00207160.2022.2141571.
I. Fernández-Tobías, I. Cantador, P. Tomeo, V. W. Anelli, and T. Di Noia, “Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization,” User Model. User-adapt. Interact., vol. 29, no. 2, pp. 443–486, 2019, doi: 10.1007/s11257-018-9217-6.
V. P, V. G, and K. S. Joseph, “A Combined Approach For Collaborative Filtering Based Recommender Systems with Matrix Factorisation and Outlier Detection,” J. Bus. Anal., vol. 4, no. 2, pp. 111–124, 2021, doi: 10.1080/2573234X.2021.1947752.
P. Zhang, Z. Zhang, T. Tian, and Y. Wang, “Collaborative filtering recommendation algorithm integrating time windows and rating predictions,” Appl. Intell., vol. 49, no. 8, pp. 3146–3157, 2019, doi: 10.1007/s10489-019-01443-2.
R. Nesmaoui, M. Louhichi, and M. Lazaar, “A Collaborative Filtering Movies Recommendation System based on Graph Neural Network,” Procedia Comput. Sci., vol. 220, no. 2019, pp. 456–461, 2023, doi: 10.1016/j.procs.2023.03.058.
S. Airen and J. Agrawal, “Movie Recommender System Using Parameter Tuning of User and Movie Neighbourhood via Co-Clustering,” Procedia Comput. Sci., vol. 218, pp. 1176–1183, 2022, doi: 10.1016/j.procs.2023.01.096.
Y. L. Chen, Y. H. Yeh, and M. R. Ma, “A movie recommendation method based on users’ positive and negative profiles,” Inf. Process. Manag., vol. 58, no. 3, p. 102531, 2021, doi: 10.1016/j.ipm.2021.102531.
J. Bobadilla, A. Gutiérrez, R. Yera, and L. Martínez, “Creating Synthetic Datasets for Collaborative Filtering Recommender Systems using Generative Adversarial Networks,” vol. 280, no. September, 2023, doi: 10.1016/j.knosys.2023.111016.
Z. Movafegh and A. Rezapour, “Improving collaborative recommender system using hybrid clustering and optimized singular value decomposition,” Eng. Appl. Artif. Intell., vol. 126, no. PD, p. 107109, 2023, doi: 10.1016/j.engappai.2023.107109.
T. Mohammadpour, A. M. Bidgoli, R. Enayatifar, and H. H. S. Javadi, “Efficient clustering in collaborative filtering recommender system: Hybrid method based on genetic algorithm and gravitational emulation local search algorithm,” Genomics, vol. 111, no. 6, pp. 1902–1912, 2019, doi: 10.1016/j.ygeno.2019.01.001.
R. Sun, A. Akella, R. Kong, M. Zhou, and J. A. Konstan, “Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment,” Int. J. Hum. Comput. Interact., vol. 0, no. 0, pp. 1–15, 2023, doi: 10.1080/10447318.2023.2262796.
S. Natarajan, S. Vairavasundaram, S. Natarajan, and A. H. Gandomi, “Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data,” Expert Syst. Appl., vol. 149, 2020, doi: 10.1016/j.eswa.2020.113248.
M. Jallouli, S. Lajmi, and I. Amous, “When contextual information meets recommender systems: extended SVD++ models,” Int. J. Comput. Appl., vol. 44, no. 4, pp. 349–356, 2022, doi: 10.1080/1206212X.2020.1752971.
Y. W. • Y. Zh. • S. Wei, “Collaborative filtering recommendation algorithm based on interval-valued fuzzy numbers,” Appl. Intell., vol. 4, no. 1, pp. 31–44, 2020, doi: 10.1108/IJCS-10-2019-0030.
S. Lee, “Fuzzy clustering with optimization for collaborative filtering-based recommender systems,” J. Ambient Intell. Humaniz. Comput., vol. 13, no. 9, pp. 4189–4206, 2022, doi: 10.1007/s12652-021-03552-8.
K. Kim, “A new similarity measure to increase coverage of rating predictions for collaborative filtering,” Appl. Intell., no. 123, 2023, doi: 10.1007/s10489-023-05041-1.
S. Kumar, K. De, and P. P. Roy, “Movie Recommendation System Using Sentiment Analysis from Microblogging Data,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 4, pp. 915–923, 2020, doi: 10.1109/TCSS.2020.2993585.
J. Parthasarathy and R. B. Kalivaradhan, “An effective content boosted collaborative filtering for movie recommendation systems using density based clustering with artificial flora optimization algorithm,” Int. J. Syst. Assur. Eng. Manag., 2021, doi: 10.1007/s13198-021-01101-2.
K. K. Jena et al., “Neural model based collaborative filtering for movie recommendation system,” Int. J. Inf. Technol., vol. 14, no. 4, pp. 2067–2077, 2022, doi: 10.1007/s41870-022-00858-4.
G. Behera and N. Nain, “The State-of-the-Art and Challenges on Recommendation System’s: Principle, Techniques and Evaluation Strategy,” SN Comput. Sci., vol. 4, no. 5, 2023, doi: 10.1007/s42979-023-02207-z.
K. Iliopoulou, A. Kanavos, A. Ilias, C. Makris, and G. Vonitsanos, Improving Movie Recommendation Systems Filtering by Exploiting User-Based Reviews and Movie Synopses, vol. 585 IFIP. Springer International Publishing, 2020.
A. Jha, N. Agarwal, D. K. Tayal, and V. A. B, Movie Recommendation Using Content-Based and Collaborative Filtering Approach, vol. 1. Springer Nature Switzerland.
A. Torkashvand, S. M. Jameii, and A. Reza, Deep learning-based collaborative filtering recommender systems: a comprehensive and systematic review, vol. 3. Springer London, 2023.
F. Berisha and E. Bytyçi, “Addressing cold start in recommender systems with neural networks: a literature survey,” Int. J. Comput. Appl., vol. 45, no. 7–8, pp. 485–496, 2023, doi: 10.1080/1206212X.2023.2237766.
Y. Koren, S. Rendle, and R. Bell, Advances in Collaborative Filtering. 2022.
M. R. Zarei and M. R. Moosavi, “A Memory-Based Collaborative Filtering Recommender System Using Social Ties,” 4th Int. Conf. Pattern Recognit. Image Anal. IPRIA 2019, pp. 263–267, 2019, doi: 10.1109/PRIA.2019.8786023.
T. Anwar and V. Uma, “Comparative study of recommender system approaches and movie recommendation using collaborative filtering,” Int. J. Syst. Assur. Eng. Manag., vol. 12, no. 3, pp. 426–436, 2021, doi: 10.1007/s13198-021-01087-x.
A. Gupta and P. Srinath, “A recommender system based on collaborative filtering, graph theory using HMM based similarity measures,” Int. J. Syst. Assur. Eng. Manag., vol. 13, no. s1, pp. 533–545, 2022, doi: 10.1007/s13198-021-01537-6.
X. Peng, H. Zhang, X. Zhou, S. Wang, X. Sun, and Q. Wang, Chestnut: Improve serendipity in movie recommendation by an information theory-based collaborative filtering approach, vol. 12185 LNCS. Springer International Publishing, 2020.
Z. Zhang, Y. Zhang, and Y. Ren, “Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering,” Inf. Retr. J., vol. 23, no. 4, pp. 449–472, 2020, doi: 10.1007/s10791-020-09378-w.
N. Pavitha et al., “Movie recommendation and sentiment analysis using machine learning,” Glob. Transitions Proc., vol. 3, no. 1, pp. 279–284, 2022, doi: 10.1016/j.gltp.2022.03.012.
S. Raza and C. Ding, News recommender system: a review of recent progress, challenges, and opportunities, vol. 55, no. 1. Springer Netherlands, 2022.
S. R. Mandalapu, B. Narayanan, and S. Putheti, “A hybrid collaborative filtering mechanism for product recommendation system,” Multimed. Tools Appl., no. 0123456789, 2023, doi: 10.1007/s11042-023-16056-8.
G. Parthasarathy and S. Sathiya Devi, “Hybrid Recommendation System Based on Collaborative and Content-Based Filtering,” Cybern. Syst., vol. 54, no. 4, pp. 432–453, 2023, doi: 10.1080/01969722.2022.2062544.
F. E. Zaizi, S. Qassimi, and S. Rakrak, “Multi-objective optimization with recommender systems: A systematic review,” Inf. Syst., vol. 117, p. 102233, 2023, doi: 10.1016/j.is.2023.102233.
M. Nasir and C. I. Ezeife, A Survey and Taxonomy of Sequential Recommender Systems for E-commerce Product Recommendation, vol. 4, no. 6. Springer Nature Singapore, 2023.
A. Ghannadrad, M. Arezoumandan, L. Candela, and D. Castelli, “Recommender Systems for Science: A Basic Taxonomy,” CEUR Workshop Proc., vol. 3160, 2022.
H. Koohi and K. Kiani, “Two new collaborative filtering approaches to solve the sparsity problem,” Cluster Comput., vol. 24, no. 2, pp. 753–765, 2021, doi: 10.1007/s10586-020-03155-6.
L. Wang, Z. Huang, Q. Pei, and S. Wang, “Federated CF: Privacy-Preserving Collaborative Filtering Cross Multiple Datasets,” IEEE Int. Conf. Commun., vol. 2020-June, 2020, doi: 10.1109/ICC40277.2020.9148791.
A. Abdolmaleki and M. H. Rezvani, “An optimal context-aware content-based movie recommender system using genetic algorithm: a case study on MovieLens dataset,” J. Exp. Theor. Artif. Intell., vol. 00, no. 00, pp. 1–27, 2022, doi: 10.1080/0952813X.2022.2153279.
and S. S. R. Prajna Paramita Parida, Mahendra Kumar Gourisaria Manjusha Pandey, Hybrid Movie Recommender System - A Proposed Model. Springer Singapore, 2022.
R. Kirubahari and S. M. J. Amali, “An improved restricted Boltzmann Machine using Bayesian Optimization for Recommender Systems,” Evol. Syst., no. 0123456789, 2023, doi: 10.1007/s12530-023-09520-1.
R. Abolghasemi, P. Engelstad, E. Herrera-Viedma, and A. Yazidi, “A personality-aware group recommendation system based on pairwise preferences,” Inf. Sci. (Ny)., vol. 595, pp. 1–17, 2022, doi: 10.1016/j.ins.2022.02.033.
Y. Lee, S. H. Kim, and K. C. Cha, “Impact of online information on the diffusion of movies: Focusing on cultural differences,” J. Bus. Res., vol. 130, no. September 2019, pp. 603–609, 2021, doi: 10.1016/j.jbusres.2019.08.044.
L. S. Al-Abbas, A. S. Haider, and B. Saideen, “A quantitative analysis of the reactions of viewers with hearing impairment to the intralingual subtitling of Egyptian movies,” Heliyon, vol. 8, no. 1, p. e08728, 2022, doi: 10.1016/j.heliyon.2022.e08728.
Y. Li, C. Chen, X. Zheng, J. Liu, and J. Wang, “Making recommender systems forget: Learning and unlearning for erasable recommendation,” Knowledge-Based Syst., vol. 283, no. August 2023, p. 111124, 2024, doi: 10.1016/j.knosys.2023.111124.
G. Jain, T. Mahara, and S. C. Sharma, “Performance Evaluation of Time-based Recommendation System in Collaborative Filtering Technique,” Procedia Comput. Sci., vol. 218, no. 2022, pp. 1834–1844, 2023, doi: 10.1016/j.procs.2023.01.161.
N. R. Kermany, W. Zhao, T. Batsuuri, J. Yang, and J. Wu, “Incorporating user rating credibility in recommender systems,” Futur. Gener. Comput. Syst., vol. 147, pp. 30–43, 2023, doi: 10.1016/j.future.2023.04.029.
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