Abstract
Recommender systems are essential tools for many e-commerce services, such as Amazon, Netflix, etc. to recommend new items to users. Among various recommendation techniques, collaborative filtering has shown tremendous performance by using the rating patterns of users. Traditional collaborative filtering, matrix factorization, and deep matrix factorization are the most representative collaborative filtering techniques. However, despite their extensive utility, the selection of the method which provides better recommendation performance is still a major concern in multi-criteria recommender systems. Most recommender systems (RSs) work only on the single criterion rating, i.e., the overall rating. Single-criterion collaborative filtering (CF) generates less reliable recommendations because it suffers from correlation-based similarity problems. However, predictions based on multiple criteria have proven more accurate. This paper compares traditional collaborative filtering, matrix factorization and deep matrix factorization in recommender systems on multi-criteria datasets. We describe details of these techniques in various aspects of recommendation quality, such as how those methods handle cold-start problems, which typically happen in collaborative filtering. We performed several experiments extensively over two real-world datasets to evaluate the performance of each method in terms of qualitative and quantitative measures and observe that deep matrix factorization techniques is superior to all other techniques.
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The datasets and code used in this paper are available from the corresponding author on reasonable request.
References
Zhao J, Geng X, Zhou J, Sun Q, Xiao Y, Zhang Z, Fu Z (2019) Attribute mapping and autoencoder neural network based matrix factorization initialization for recommendation systems. Knowl-Based Syst 166:132–139
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749
Adomavicius G, Manouselis N, Kwon Y (2010) Multi-criteria recommender systems. In: Recommender systems handbook. Boston, MA: Springer US, pp 769–803
Zhang S, Yao L, Sun A, Tay Y (2019) Deep learning based recommender system: A survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38
Fu M, Qu H, Yi Z, Lu L, Liu Y (2018) A novel deep learning-based collaborative filtering model for recommendation system. IEEE Trans Cybern. https://doi.org/10.1109/tcyb.2018.2795041
Lee J, Sun M, Lebanon G (2012) A comparative study of collaborative filtering algorithms. arXiv preprint arXiv:1205.3193
Mahajan S, Pande A (2014) Mining Web Graphs for
Billsus D, Pazzani MJ (1998) Learning collaborative information filters. In: Icml 98:46–54
Bokde D, Girase S, Mukhopadhyay D (2015) Matrix factorization model in collaborative filtering algorithms: A survey. Proc Comput Sci 49:136–146
Adomavicius G, Kwon Y (2007) New recommendation techniques for multicriteria rating systems. IEEE Intell Syst 22(3):48–55
Si L, Jin R (2003) Flexible mixture model for collaborative filtering. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03) pp 704–711
Sahoo N, Krishnan R, Duncan G, Callan J (2012) Research note—the halo effect in multicomponent ratings and its implications for recommender systems: The case of yahoo! movies. Inf Syst Res 23(1):231–246
Hassan M, Hamada M (2017) A neural networks approach for improving the accuracy of multi-criteria recommender systems. Appl Sci 7(9):868
Hassan M, Hamada M (2018) Genetic algorithm approaches for improving prediction accuracy of multi-criteria recommender systems. Int J Comput Intell Syst 11(1):146–162
Roy B (1996) Multicriteria methodology for decision aiding, vol 12. Springer Science & Business Media
Lakiotaki K, Tsafarakis S, Matsatsinis N (2008) UTA-Rec: a recommender system based on multiple criteria analysis. In: Proceedings of the 2008 ACM conference on Recommender systems pp 219–226
Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th international conference on Machine learning pp 791–798
Georgiev K, Nakov P (2013) A non-iid framework for collaborative filtering with restricted boltzmann machines. In: International conference on machine learning, pp 1148–1156. PMLR
Sinha BB, Dhanalakshmi R (2022) DNN-MF: Deep neural network matrix factorization approach for filtering information in multi-criteria recommender systems. Neural Comput Appl 34(13):10807–10821
He X, Liao L, Zhang H, Nie L, Hu X, Chua TS (2017) Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web pp 173–182
Nassar N, Jafar A, Rahhal Y (2020) A novel deep multi-criteria collaborative filtering model for recommendation system. Knowl-Based Syst 187:104811
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All authors contributed to the study conception and design. Pragya Dwivedi and Vibhor Kant prepared the material preparation, collected and analyzed data. Reetu Singh prepared figures and wrote the first draft of the manuscript, and all authors provide feedback on previous versions of the manuscript. All authors read and approved the final manuscript.
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Singh, R., Dwivedi, P. & Kant, V. Comparative analysis of collaborative filtering techniques for the multi-criteria recommender systems. Multimed Tools Appl 83, 64551–64571 (2024). https://doi.org/10.1007/s11042-024-18164-5
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DOI: https://doi.org/10.1007/s11042-024-18164-5