JACIII Vol.24 No.6 pp. 719-727
doi: 10.20965/jaciii.2020.p0719


Matrix-Based Collaborative Filtering Employing Personal Values-Based Modeling and Model Relationship Learning

Yasufumi Takama, Hiroki Shibata, and Yuya Shiraishi

Graduate School of Systems Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

February 18, 2020
July 27, 2020
November 20, 2020
recommendation, personal values, matrix factorization, collaborative filtering, BPR

This paper proposes a matrix-based collaborative filtering (CF) employing personal values (MCFPV). Introduction of various factors such as diversity and long-tailedness in addition to accuracy is a recent trend in the study of recommender systems. We think recommending acceptable items while satisfying users’ preference is important when considering other factors than accuracy. Also, interpretability is one of important characteristics recommender systems should have. To recommend acceptable items on the basis of an interpretable mechanism, this paper proposes a matrix-based recommendation method based on personal values-based modeling. Whereas existing CF based on matrix factorization methods are known to be more accurate than neighborhood-based CF, latent factors obtained by existing methods are difficult to interpret. On the other hand, user/item models of the propose method (MCFPV) is expected to be interpretable, because it represents the effect of each attribute items have on user’s decision making. Regarding a model relationship matrix that connects user and item models, this paper proposes two approaches: manual setting and machine learning approaches. Experimental results using 5 datasets generated from actual review sites show that the proposed methods recommend much unpopular items than the state-of-the art matrix factorization-based methods while keeping precision and recall.

Cite this article as:
Yasufumi Takama, Hiroki Shibata, and Yuya Shiraishi, “Matrix-Based Collaborative Filtering Employing Personal Values-Based Modeling and Model Relationship Learning,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.6, pp. 719-727, 2020.
Data files:
  1. [1] P. Adamopoulos and A. Tuzhilin, “On over-specialization and concentration bias of recommendations: probabilistic neighborhood selection in collaborative filtering systems,” Proc. of the 8th ACM Conf. on Recommender Systems (RecSys’14), pp. 153-160, 2014.
  2. [2] D. M. Fleder and K. Hosanagar, “Recommender systems and their impact on sales diversity,” Proc. of the 8th ACM Conf. on Electronic Commerce (EC’07), pp. 192-199, 2007.
  3. [3] R. Krestel and N. Dokoohaki, “Diversifying Product Review Rankings: Getting the Full Picture,” Proc. of the 2011 IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology (WI-IAT 2011), pp. 138-145, 2011.
  4. [4] W. Wu, L. Chen, and L. He, “Using personality to adjust diversity in recommender systems,” Proc. of the 24th ACM Conf. on Hypertext and Social Media (HT’13), pp. 225-229, 2013.
  5. [5] C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” Proc. of the 14th Int. Conf. on World Wide Web (WWW’05), pp. 22-32, 2005.
  6. [6] J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl, “Evaluating collaborative filtering recommender systems,” ACM Trans. on Information Systems, Vol.22, No.1, pp. 5-53, 2004.
  7. [7] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” Proc. of the 10th Int. Conf. on World Wide Web (WWW’01), pp. 285-295, 2001.
  8. [8] D. Jannach, L. Lerche, F. Gedikli, and G. Bonnin, “What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects,” Proc. of the 21st Int. Conf. on User Modeling, Adaptation, and Personalization (UMAP 2013), pp. 25-37, 2013.
  9. [9] Y.-J. Park and A. Tuzhilin, “The long tail of recommender systems and how to leverage it,” Proc. of the 2008 ACM Conf. on Recommender Systems (RecSys’08), pp. 11-18, 2008.
  10. [10] L. Shi, “Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach,” Proc. of the 7th ACM Conf. on Recommender Systems (RecSys’13), pp. 57-64, 2013.
  11. [11] D. Elsweiler, C. Trattner, and M. Harvey, “Exploiting Food Choice Biases for Healthier Recipe Recommendation,” Proc. of the 40th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR’17), pp. 575-584, 2017.
  12. [12] C. Porcel, A. Tejeda-Lorente, M. A. Martínez, and E. Herrera-Viedma, “A hybrid recommender system for the selective dissemination of Research Resources in a Technology Transfer Office,” Information Sciences, Vol.184, Issue 1, pp. 1-19, 2012.
  13. [13] P. Cremonesi, Y. Koren, and R. Turrin, “Performance of recommender algorithms on top-n recommendation tasks,” Proc. of the 4th ACM Conf. on Recommender Systems (RecSys’10), pp. 39-46, 2010.
  14. [14] U. Shardanand and P. Maes, “Social information filtering: algorithms for automating “word of mouth”,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems (CHI’95), pp. 210-217, 1995.
  15. [15] N. Tintarev and J. Masthoff, “A Survey of Explanations in Recommender Systems,” Proc. of the 2007 IEEE 23rd Int. Conf. on Data Engineering Workshop (ICDE’07), pp. 801-810, 2007.
  16. [16] S. Hattori and Y. Takama, “Recommender System Employing Personal-Value-Based User Model,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.2, pp. 157-165, 2014.
  17. [17] Y. Takama, R. Misawa, Y.-S. Chen, S. Hattori, and H. Ishikawa, “Proposal of Hybrid Recommender Systems Based on Personal Values-based Collaborative Filtering,” 7th Int. Symp. on Computational Intelligence and Industrial Applications (ISCIIA 2016), SM-GS3-01, 2016.
  18. [18] Y. Takama, T. Yamaguchi, and S. Hattori, “Personal Values-Based Item Modeling and its Application to Recommendation with Explanation,” J. Adv. Comput. Intell. Intell. Inform., Vol.20, No.6, pp. 867-874, 2016.
  19. [19] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Application of Dimensionality Reduction in Recommender System – A Case Study,” ACM WebKDD 2000 Workshop, 2000.
  20. [20] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” Proc. of the 13th Int. Conf. on Neural Information Processing Systems (NIPS’00), pp. 556-562, 2000.
  21. [21] R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization,” Proc. of the 20th Int. Conf. on Neural Information Processing Systems (NIPS’07), pp. 1257-1264, 2007.
  22. [22] S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme, “BPR: Bayesian personalized ranking from implicit feedback,” Proc. of the 25th Conf. on Uncertainty in Artificial Intelligence (UAI’09), pp. 452-461, 2009.
  23. [23] Y. Shiraishi and Y. Takama, “Proposal on matrix-based collaborative filtering using personal values,” Proc. of the 2017 IEEE 10th Int. Workshop on Computational Intelligence and Applications (IWCIA 2017), pp. 55-60, 2017.
  24. [24] Y. Takama, H. Shibata, and Y. Shiraishi, “Introduction of Model Relationship Learning for Matrix-Based Collaborative Filtering Employing Personal Values,” 6th Int. Workshop on Advanced Computational Intelligence and Intelligent Informatics (IWACIII2019), SUN4-B6, 2019.
  25. [25] Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, Vol.42, No.8, pp. 30-37, 2009.
  26. [26] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” Proc. of the 1994 ACM Conf. on Computer Supported Cooperative Work (CSCW’94), pp. 175-186, 1994.
  27. [27] D. E. Vinson, J. E. Scott, and L. M. Lamont, “The Role of Personal Values in Marketing and Consumer Behavior,” J. of Marketing, Vol.41, No.2, pp. 44-50, 1977.
  28. [28] J. Chen, G. Hsieh, J. U. Mahmud, and J. Nichols, “Understanding individuals’ personal values from social media word use,” Proc. of the 17th ACM Conf. on Computer Supported Cooperative Work & Social Computing (CSCW’14), pp. 405-414, 2014.
  29. [29] S. Kabbur, X. Ning, and G. Karypis, “FISM: factored item similarity models for top-N recommender systems,” Proc. of the 19th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’13), pp. 659-667, 2013.
  30. [30] A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver, “Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering,” Proc. of the 4th ACM Conf. on Recommender Systems (RecSys’10), pp. 79-86, 2010.
  31. [31] P. Filzmoser, B. Liebmann, and K. Varmuza, “Repeated double cross validation,” J. of Chemometrics, Vol.23, No.4, pp. 160-171, 2009.
  32. [32] L. Liu and M. T. Özsu (Eds.), “Encyclopedia of Database Systems,” 1st edition, Springer, 2009.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Mar. 01, 2021