JACIII Vol.28 No.1 pp. 111-121
doi: 10.20965/jaciii.2024.p0111

Research Paper:

Personal Value-Based User Modeling Without Attribute Evaluation and its Application to Collaborative Filtering

Kaichi Nihira ORCID Icon, Hiroki Shibata ORCID Icon, and Yasufumi Takama ORCID Icon

Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

March 15, 2023
September 6, 2023
January 20, 2024
recommendation, personal values, collaborative filtering

This paper proposes a personal values modeling method that does not require attribute ratings. The proposed method is applied to memory-based and model-based collaborative filtering (CF) to demonstrate its effectiveness. A recent trend in CF is to introduce additional factors than interaction history. A rate matching rate (RMRate) has been proposed for modeling user’s personal values, and it has been shown to be effective in increasing diversity and recommending niche (long-tail or unpopular) items. However, RMRate needs an attribute-level evaluations in addition to rating (total evaluation) to items, which limits its applicability. To obtain users’ personal values model only from a rating matrix, this paper defines users’ personal values as their tendency to select popular/unpopular items and reputable/unreputable items. Ten attributes are proposed to model user’s personal values, all of which can be calculated from a rating matrix without additional information. Experimental results on four datasets show that the proposed attributes have different characteristics from the RMRate, and can improve precision, recall, and normalized discounted cumulative gain of memory-based CF and factorization machines. It is also shown that the proposed modeling method is useful for mitigating a cold-start problem.

Cite this article as:
K. Nihira, H. Shibata, and Y. Takama, “Personal Value-Based User Modeling Without Attribute Evaluation and its Application to Collaborative Filtering,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.1, pp. 111-121, 2024.
Data files:
  1. [1] F. Pachet, P. Roy, and D. Cazaly, “A Combinatorial Approach to Content-Based Music Selection,” IEEE MultiMedia, Vol.7, No.1, pp. 44-51, 2000.
  2. [2] P. Resnick et al., “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.
  3. [3] A. I. Schein et al., “Methods and Metrics for Cold-Start Recommendations,” Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR’02), pp. 253-260, 2002.
  4. [4] S. Lee, J. Yang, and S.-Y. Park, “Discovery of Hidden Similarity on Collaborative Filtering to Overcome Sparsity Problem,” Proc. of the 7th Int. Conf. on Discovery Science, pp. 396-402, 2004.
  5. [5] 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.
  6. [6] J. Chen et al., “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.
  7. [7] 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.
  8. [8] Y. Takama, Y.-S. Chen, R. Misawa, and H. Ishikawa, “Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.4, pp. 506-513, 2018.
  9. [9] Y. Takama, H. Shibata, and Y. 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.
  10. [10] K. Nihira, H. Shibata, and Y. Takama, “Proposal of Personal Value-Based User Modeling Without Attribute Evaluation,” The 10th Int. Symp. on Computational Intelligence and Industrial Applications (ISCIIA 2022), Session No.C1-3, 2022.
  11. [11] G. Linden, B. Smith, and J. York, “ Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Vol.7, No.1, pp. 76-80, 2003.
  12. [12] S. Rendle, “Factorization Machines,” 2010 IEEE Int. Conf. on Data Mining, pp. 995-1000, 2010.

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

Last updated on Feb. 19, 2024