JACIII Vol.24 No.3 pp. 326-334
doi: 10.20965/jaciii.2020.p0326


User Modeling from Review Browsing History for Personal Values-Based Recommendation

Yasufumi Takama and Suzuto Shimizu

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

September 12, 2019
February 26, 2020
May 20, 2020
information recommendation, personal values, user modeling, online reviews

This paper proposes a personal values-based user modeling method from user’s browsing history of reviews. Personal values-based user modeling and its application to recommender systems have been studied. This approach models users’ personal values as the effect of item’s attributes on their decision making. While existing method obtains a user model from reviews posted by a user, this paper proposes to obtain it from reviews a user consulted for his/her decision making. Methods for determining reviews to present for obtaining user feedback, as well as for selecting items to recommend are proposed, of which effectiveness are shown with user experiments.

Procedure of modeling process

Procedure of modeling process

Cite this article as:
Y. Takama and S. Shimizu, “User Modeling from Review Browsing History for Personal Values-Based Recommendation,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.3, pp. 326-334, 2020.
Data files:
  1. [1] H. Ishikawa, “Social Big Data Mining,” CRC Press, 2015.
  2. [2] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” Proc. 1994 ACM Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994.
  3. [3] G. Linden, B. Smith, and J. York, “ Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Vol.7, No.1, pp. 76-80, 2003.
  4. [4] Y. Koren, R. Bell, and C. Volinsky, “Matrix Factorization Techniques for Recommender Systems,” Computer, Vol.42, No.8, pp. 30-37, 2009.
  5. [5] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” Proc. 13th Int. Conf. on Neural Information Processing Systems, pp. 535-541, 2000.
  6. [6] R. Salakhutdinov and A. Mnih, “Probabilistic Matrix Factorization,” Proc. 20th Int. Conf. on Neural Information Processing Systems, pp. 1257-1264, 2007.
  7. [7] F. Fouss, A. Pirotte, J.-M. Renders, and M. Saerens, “Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation,” IEEE Trans. on Knowledge and Data Engineering, Vol.19, No.3, pp. 355-369, 2007.
  8. [8] J. Bobadilla, F. Ortega, A. Hernando, and J. Bernal, “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Systems, Vol.26, pp. 225-238, 2012.
  9. [9] P. Massa and P. Avesani, “Trust-aware Collaborative Filtering for Recommender Systems,” On the Move to Meaningful Internet Systems 2004, Lecture Notes in Computer Science, Vol.3290, pp. 492-508, 2004.
  10. [10] S.-T. Park, D. Pennock, O. Madani, N. Good, and D. DeCoste, “Naïve Filterbots for Robust Cold-start Recommendations,” Proc. 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 699-705, 2006.
  11. [11] 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.
  12. [12] M. A. S. N. Nunes and R. Hu, “Personality-based recommender systems: an overview,” Proc. 6th ACM Conf. on Recommender Systems, pp. 5-6, 2012.
  13. [13] 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.
  14. [14] Y. Takama, R. Misawa, Y.-S. Chen, S. Hattori, and H. Ishikawa, “Proposal of Hybrid Recommender Systems Based on Personal Values-based Collaborative Filtering,” Proc. 7th Int. Symp. on Computational Intelligence and Industrial Applications, SM-GS3-01, 2016.
  15. [15] Y. Takama, T. Yamaguchi, and S. Hatori, “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.
  16. [16] G. Adomavicius and Y. Kwon, “New Recommendation Techniques for Multicriteria Rating Systems,” IEEE Intelligent Systems, Vol.22, No.3, pp. 48-55, 2007.
  17. [17] I. Titov and R. McDonald, “Modeling online reviews with multi-grain topic models,” Proc. 17th Int. Conf. on World Wide Web, pp. 111-120, 2008.
  18. [18] I. Titov and R. McDonald, “A Joint Model of Text and Aspect Ratings for Sentiment Summarization,” Proc. 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 308-316, 2008.
  19. [19] R. Catherine and W. Cohen, “TransNets: Learning to Transform for Recommendation,” Proc. 11th ACM Conf. on Recommender Systems, pp. 288-296, 2017.
  20. [20] J. McAuley and J. Leskovec, “Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text,” Proc. 7th ACM Conf. on Recommender Systems, pp. 165-172, 2013.
  21. [21] C. Jayawardhena, “Personal values’ influence on e-shopping attitude and behaviour,” Internet Research, Vol.14, No.2, pp. 127-138, 2004.
  22. [22] W. Wu, L. Chen, and L. He, “Using personality to adjust diversity in recommender systems,” Proc. 24th ACM Conf. on Hypertext and Social Media, pp. 225-229, 2013.
  23. [23] M. Rokeach, “The Nature of Human Values,” The Free Press, 1973.
  24. [24] P. T. Costa and R. R. McCrae, “Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI),” Psychological Assessment Resources, 1992.
  25. [25] X. Yan, J. Guo, Y. Lan, and X. Cheng, “A biterm topic model for short texts,” Proc. 22nd Int. Conf. on World Wide Web, pp. 1445-1456, 2013.

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

Last updated on Jul. 23, 2024