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Improving Incremental Nonnegative Matrix Factorization Method for Recommendations Based on Three-Way Decision Making

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Abstract

Nonnegative matrix factorization is comprehensively used in recommendation systems. In an effort to reduce the recommended cost of newly added samples, incremental nonnegative matrix factorization and its variants have been extensively studied in recommendation systems. However, the recommendation performance is incapable of particular applications in terms of data sparsity and sample diversity. In this paper, we propose a new incremental recommend algorithm by improving incremental nonnegative matrix factorization based on three-way decision, called Three-way Decision Recommendations Based on Incremental Non-negative Matrix Factorization (3WD-INMF), in which the concept of positive, negative, and boundary regions are employed to update the new coming samples’ features. Finally, experiments on six public data sets demonstrate the error induced by 3WD-INMF is decreasing as the addition of new samples and deliver state-of-the-art performance compared with existing recommendation algorithms. The results indicate our method is more reasonable and efficient by leveraging the idea of three-way decision to perform the recommendation decision process.

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Notes

  1. https://www.yelp.com/dataset/challenge

  2. http://jmcauley.ucsd.edu/data/amazon.

  3. https://grouplens.org/datasets/movielens/

References

  1. He Y, Wang C, Jiang C. Correlated matrix factorization for recommendation with implicit feedback. IEEE Trans Knowl Data Eng. 2019;31:451-C464.

    Article  Google Scholar 

  2. Lee DD, Seung HS. Learning the parts of objects by non-negative matrix factorization. Nature. 1999;401(6755):788–791.

    Article  MATH  Google Scholar 

  3. He M, Zhang J, Yang P, Yao K. Robust transfer learning for cross-domain collaborative filtering using multiple rating patterns approximation. In: the Eleventh ACM International Conference. 2018:225–33.

  4. Luo X, Zhou MC, Xia YN, Zhu Q. An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans Industr Inform. 2014;10(2):1273–84.

    Article  Google Scholar 

  5. Sun J, Wang Z, Li H, Sun F. Incremental nonnegative matrix factorization with sparseness constraint for image representation. Advances in Multidedia Information Processing. 2018:351–60.

  6. Hedjam R, Abdesselam A, Melgani F. NMF with feature relationship preservation penalty term for clustering problems low-rank matrix factorization. Pattern Recogn. 2021;112(107814):1–11.

    Google Scholar 

  7. Bucak SS, Gunsel B. Incremental subspace learning via non-negative matrix factorization. Pattern Recogn. 2009;42(5):788–97.

    Article  MATH  Google Scholar 

  8. Chen WS, Pan B, Fang B, Li M, Tang J. Incremental nonnegative matrix factorization for face recognition. Math Probl Eng. 2008. https://doi.org/10.1155/2008/410674.

    Article  MathSciNet  MATH  Google Scholar 

  9. Yu ZZ, Liu YH, Li B, Pang BC, Jia CC. Incremental graph regulated nonnegative matrix factorization for face recognition. J Appl Math. 2014:1–10.

  10. Zhang C, Wang H, Yang S, Gao Y, Incremental nonnegative matrix factorization based on matrix sketching and k-means clustering. Intelligent Data Engineering and Automated Learning (IDEAL), 2016 17th International Conference. 2016:426–35.

  11. Dang S, Cui ZY, Cao ZJ, Liu NY. Sar target recognition via incremental nonnegative matrix factorization with lp sparse constraint. Radar Conference. 2017;10(3):374.

    Google Scholar 

  12. Zhang XX, Chen DG, Wu KS. Incremental nonnegative matrix factorization based on correlation and graph regularization for matrix completion. Int J Mach Learn Cybern. 2019;10(6):1259–68.

    Article  Google Scholar 

  13. Liu WQ, Luo LK, Peng H, Zhang LM, Wen W, et al. A three-stage method for batch-based incremental nonnegative matrix factorization. Neurocomputing. 2020:150–60.

  14. Nguyen ST, Kwak HY, Lee SH, et al. Using stochastic gradient decent algorithm for incremental matrix factorization in recommendation system. 2019 20th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. 2019:22–35.

  15. Lei S, Li D, Yang Y. IncRMF: an incremental recommendation algorithm based on regularized matrix factorization. In Proceedings of the 2018 2nd International Conference on Big Data and Internet of Things. 2018:98–107.

  16. Ye X, Liu D. An interpretable sequential three-way recommendation based on collaborative topic regression. Expert Syst Appl. 2021;14(114454):17–35.

    Google Scholar 

  17. Ye X, Liu D, Liang D. Three-way granular recommendation algorithm based on collaborative filtering. Comp Sci. 2018;45(1):90–6.

    Google Scholar 

  18. Qian F, Min Q, Zhao S, Chen J, Wang X, Zhang Y. Three-way decision collaborative recommendation algorithm based on user reputation. Rough Sets - International Joint Conference (IJCRS). 2019:424–38.

  19. Moshe T, Oren K. Rethinking search engines and recommendation systems: a game theoretic perspective. Commun ACM. 2019;62(12):66–75.

    Article  Google Scholar 

  20. Xu L, Jiang CX, Chen Y, Ren Y, J. R., Liu R. User participation in collaborative filtering-based recommendation systems: game theoretic approach. IEEE Trans Cybern. 2019;4(29):1339–51.

  21. Zacharoula KP, Anastasios AE. Motivating students in collaborative activities with game-theoretic group recommendations. IEEE Trans Learn Technol. 2020;3(23):374–86.

    Google Scholar 

  22. Azam N, Yao JT. Game-theoretic rough sets for recommender systems. Knowl-Based Syst. 2014;72(83):96–107.

    Article  Google Scholar 

  23. Chiu MC, Chen T. Assessing mobile and smart technology applications for active and healthy aging using a fuzzy collaborative intelligence approach. Cogn Comput. 2021;13(2):431–46.

    Article  Google Scholar 

  24. Cao H. The utilization of rough set theory and data reduction based on artificial intelligence in recommendation system. Soft Comput. 2020;4(33):21–40.

    Google Scholar 

  25. Hammou BA, Lahcen AA, Mouline S. An effective distributed predictive model with matrix factorization and random forest for big data recommendation systems. Expert Syst Appl. 2019;93(21):253–65.

    Article  Google Scholar 

  26. Sami B, Kamel B, Omar B. Expertise-aware news feed updates recommendation: a random forest approach. Clust Comput. 2020;109(225):2375–88.

    Google Scholar 

  27. Zhang HR, Min F. Three-way recommender systems based on random forests. Knowl-Based Syst. 2016;91(1016):275–86.

    Article  Google Scholar 

  28. Liu D, Ye X. A matrix factorization based dynamic granularity recommendation with three-way decisions. Knowl-Based Syst. 2020;22(105243):22–47.

    Google Scholar 

  29. Yao YY. The superiority of three-way decisions in probabilistic rough set models. Inf Sci. 2011;181(6):1080–96.

    Article  MathSciNet  MATH  Google Scholar 

  30. Yao YY. Three-way decisions with probabilistic rough sets. Inf Sci. 2010;180(3):341–53.

    Article  MathSciNet  Google Scholar 

  31. Mao Y, Saul LK. Modeling distances in large-scale networks by matrix factorization. Proceedings of the 4th ACM SIGCOMM conference on Internet measurement. 2004:278–87.

  32. Paatero P, Tapper U. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics. 1994;5(2):111–26.

    Article  Google Scholar 

  33. Wang N, Wang HN, Jia YL, Yin Y. Explainable recommendation via multi-task learning in opinionated text data. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieva (SIGIR). 2018:165–74.

  34. Tao YY, Jia YL, Wang N, Wang HN. The fact: Taming latent factor models for explainability with factorization trees. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR). 2019:295–304.

  35. Cai D, He X, Han J, Huang TS. Graph regularized nonnegative matrix factorization for data representation. IEEE Trans Pattern Anal Mach Intell. 2011;33(8):1548–60.

    Article  Google Scholar 

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Funding

This work is supported by the State Key Program of National Nature Science Foundation of China (61936001), the National Key R&D Program of China (2019YFB2103000), National Nature Science Foundation of China (61876027), Natural Science Foundation of Chongqing (cstc2019jcyj-cxttX0002), the Science and Technology Research Program of Chongqing Education Commission of China (KJQN201900638).

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Authors and Affiliations

  1. Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, 400065, PR China

    Xiaoxia Zhang, Lu Chen, Ye Wang & Guoyin Wang

Authors
  1. Xiaoxia Zhang
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  2. Lu Chen
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  3. Ye Wang
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  4. Guoyin Wang
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Correspondence to Xiaoxia Zhang or Guoyin Wang.

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Cite this article

Zhang, X., Chen, L., Wang, Y. et al. Improving Incremental Nonnegative Matrix Factorization Method for Recommendations Based on Three-Way Decision Making. Cogn Comput 14, 1978–1996 (2022). https://doi.org/10.1007/s12559-021-09897-8

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  • DOI: https://doi.org/10.1007/s12559-021-09897-8

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