--

14(1)2024

Dynamic matrix factorization-based collaborative filtering in movie recommendation services


Author - Affiliation:
Vuong Luong Nguyen - FPT University, Danang , Vietnam
Trinh Quoc Vo - FPT University, Danang , Vietnam
Hoai Thi Thuy Nguyen - FPT University, Danang , Vietnam
Corresponding author: Vuong Luong Nguyen - vuongnl3@fe.edu.vn
Submitted: 21-08-2023
Accepted: 23-10-2023
Published: 05-03-2024

Abstract
Movies are a primary source of entertainment, but finding specific content can be challenging given the exponentially increasing number of movies produced each year. Recommendation systems are extremely useful for solving this problem. While various approaches exist, Collaborative Filtering (CF) is the most straightforward. CF leverages user input and historical preferences to determine user similarity and suggest movies. Matrix Factorization (MF) is one of the most popular Collaborative Filtering (CF) techniques. It maps users and items into a joint latent space, using a vector of latent features to represent each user or item. However, traditional MF techniques are static, while user cognition and product variety are constantly evolving. As a result, traditional MF approaches struggle to accommodate the dynamic nature of user-item interactions. To address this challenge, we propose a Dynamic Matrix Factorization CF model for movie recommendation systems (DMF-CF) that considers the dynamic changes in user interactions. To validate our approach, we conducted evaluations using the standard MovieLens dataset and compared it to state-of-the-art models. Our preliminary findings highlight the substantial benefits of DMF-CF, which outperforms recent models on the MovieLens-100K and MovieLens-1M datasets in terms of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics.

Keywords
collaborative filtering; matrix factorization; recommendation system

Full Text:
PDF

Cite this paper as:

Nguyen, V. L., Vo, T. Q., & Nguyen, H. T. T. (2024). Dynamic matrix factorization-based collaborative filtering in movie recommendation services. Ho Chi Minh City Open University Journal of Science – Engineering and Technology, 14(1), 3-12. doi:10.46223/HCMCOUJS.tech.en.14.1.2922.2024


References

Aciar, S. V., Aciar, G. I., Collazos, C. A., & Gonzalez, C. S. (2016). User recommender system based on knowledge, availability, and reputation from interactions in forums. IEEE Revista Iberoamericana de Tecnologias Del Aprendizaje, 11(1), 18-22. doi:10.1109/RITA.2016.2518441


Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. doi:10.1109/TKDE.2005.99


Aghdam, M. H., Analoui, M., & Kabiri, P. (2017). Collaborative filtering using non-negative matrix factorisation. Journal of Information Science, 43(4), 567-579. doi:10.1177/0165551516654354


Anwar, T., Uma, V., & Srivastava, G. (2021). Rec-CFSVD++: Implementing recommendation system using collaborative filtering and Singular Value Decomposition (SVD)++. International Journal of Information Technology & Decision Making, 20(4), 1075-1093. doi:10.1142/S0219622021500310


Ding, Y., & Li, X. (2005). Time weight collaborative filtering. In Proceedings of the 14th ACM international conference on Information and knowledge management (pp. 485-492). New York, NY: ACM.


Harper, F. M., & Konstan, J. A. (2016). The movielens datasets. ACM Transactions on Interactive Intelligent Systems, 5(4), 1-19. doi:10.1145/2827872


He, X., Zhang, H., Kan, M.-Y., & Chua, T.-S. (2016). Fast matrix factorization for online recommendation with implicit feedback. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval (pp. 549-558). New York, NY: ACM.


Karahodza, B., Supic, H., & Donko, D. (2014). An approach to design of time-aware recommender system based on changes in group user’s preferences. In Proceedings of 2014 X International Symposium on Telecommunications (BIHTEL) (pp. 1-4). Piscataway, NJ: IEEE.


Koren, Y. (2008). Factorization meets the neighborhood. In Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 426-434). New York, NY: ACM.


Koren, Y. (2009). Collaborative filtering with temporal dynamics. In Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 447-456). New York, NY: ACM.


Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37. doi:10.1109/MC.2009.263


Lathia, N., Hailes, S., & Capra, L. (2009). Temporal collaborative filtering with adaptive neighbourhoods. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 796-797). New York, NY: ACM.


Lee, T. Q., Park, Y., & Park, Y.-T. (2008). A time-based approach to effective recommender systems using implicit feedback. Expert Systems with Applications, 34(4), 3055-3062. doi:10.1016/j.eswa.2007.06.031


Lee, T. Q., Park, Y., & Park, Y.-T. (2009). An empirical study on effectiveness of temporal information as implicit ratings. Expert Systems with Applications, 36(2), 1315-1321. doi:10.1016/j.eswa.2007.11.047


Liu, N. N., Zhao, M., Xiang, E., & Yang, Q. (2010). Online evolutionary collaborative filtering. In Proceedings of the fourth ACM conference on Recommender systems (pp. 95-102). New York, NY: ACM.


Meng, J., Zheng, Z., Tao, G., & Liu, X. (2016). User-specific rating prediction for mobile applications via weight-based matrix factorization. In Proceedings of 2016 IEEE International Conference on Web Services (ICWS) (pp. 728-731). Piscataway, NJ: IEEE.


Nguyen, V. L., & Jung, J. J. (2020). Crowdsourcing platform for collecting cognitive feedbacks from users: A case study on movie recommender system. In Springer series in reliability engineering book series (pp. 139-150). Singapore: Springer.


Nguyen, V. L., Hong, M.-S., Jung, J. J., & Sohn, B.-S. (2020). Cognitive similarity-based collaborative filtering recommendation system. Applied Sciences, 10(12), Article 4183. doi:10.3390/app10124183


Nguyen, V. L., Nguyen, H. T., Jung, J. J., & Camacho, D. (2023). Extending collaborative filtering recommendation using word embedding: A hybrid approach. Concurrency and Computation: Practice and Experience, 35(16), 38-49. doi:10.1002/cpe.6232


Nguyen, V. L., Nguyen, H. T., & Jung, J. J. (2020). Content-based collaborative filtering using word embedding. In Proceedings of the International Conference on Research in Adaptive and Convergent Systems (pp. 96-100). New York, NY: ACM.


Nguyen, V. L., Vo, T. Q., & Nguyen, H. T. (2023). Adaptive KNN-based extended collaborative filtering recommendation services. Big Data and Cognitive Computing, 7(2), Article 106. doi:10.3390/bdcc7020106


Raza, S., & Ding, C. (2022). News recommender system: A review of recent progress, challenges, and opportunities. Artificial Intelligence Review, 55(16), 749-800. doi:10.1007/s10462-021-10043-x


Ricci, F., & Nguyen, N. Q. (2007). Acquiring and revising preferences in a critique-based mobile recommender system. IEEE Intelligent Systems, 22(3), 22-29. doi:10.1109/MIS.2007.43


Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1), Article 59. doi:10.1186/s40537-022-00592-5


Takács, G., Pilászy, I., Németh, B., & Tikk, D. (2008). Matrix factorization and neighbor based algorithms for the Netflix prize problem. In Proceedings of the 2008 ACM conference on Recommender systems (pp. 267-274). New York, NY: ACM.


Wang, Y., Ma, W., Zhang, M., Liu, Y., & Ma, S. (2023). A survey on the fairness of recommender systems. ACM Transactions on Information Systems, 41(3), 1-43. doi:10.1145/3547333



DOI: https://doi.org/10.46223/HCMCOUJS.tech.en.14.1.2922.2024
Creative Commons License
© The Author(s) 2024. This is an open access publication under CC BY NC licence.