Personal Values-Based Item Modeling and its Application to Recommendation with Explanation

Yasufumi Takama; Takayuki Yamaguchi; Shunichi Hattori

doi:10.20965/jaciii.2016.p0867

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JACIII Vol.20 No.6 pp. 867-874

doi: 10.20965/jaciii.2016.p0867

(2016)

Paper:

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Personal Values-Based Item Modeling and its Application to Recommendation with Explanation

Yasufumi Takama^, Takayuki Yamaguchi^, and Shunichi Hattori^**

^*Graduate School of System Design, Tokyo Metropolitan University
6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan

^**Center Research Institute of Electric Power Industry
2-11-1 Iwadokita, Komae-shi, Tokyo 201-8511, Japan

Received:

March 20, 2016

Accepted:

June 21, 2016

Published:

November 20, 2016

Keywords:

recommender systems, personal values, item model, association rule

Abstract

This paper proposes a personal-value based item modeling, which is used for calculating predicted ratings and for explaining recommendation. Personal value is one of factors affecting our decision making, and its application to recommender systems has been studied recently. This paper extends existing personal values-based user modeling to item modeling, which estimates characteristics of reviewers who like / dislike target items. A method for calculating predicted ratings based on obtained personal values-based item models is also proposed. Furthermore, this paper focuses on explanation of recommendation as well, which is one of challenges in the recent study of recommender systems. Improvements of user’s satisfactions for recommender systems by showing process of recommendation gets to be important in addition to precision of recommendation. A recommender system is developed based on the proposed method, of which effectiveness is evaluated by a user experiment, in which the target items are movies. Experimental results showed the effectiveness of the proposed method including recommendation accuracy and an explanation of recommendation. It is also shown that the proposed recommender system has the potential to recommend long-tail items.

Cite this article as:

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.

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References

[1] S. Hattori and Y. Takama, “Recommender System Employing Personal-value-based User Model,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.18, No.2, pp. 157-165, 2014.
[2] A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, “Methods and metrics for cold-start recommendations,” Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 253-260, 2002.
[3] S. Lee, J. Yang, and S. Park, “Discovery of hidden similarity on collaborative filtering to overcome sparsity problem,” Discovery Science, Springer Berlin Heidelberg, Vol.3245, pp. 396-402, 2004.
[4] N. Tintarev and J. Masthoff, “A survey of explanations in recommender systems,” IEEE 23rd Int. Conf. on Data Engineering Workshop, pp. 801-810, 2007.
[5] T. Yamaguchi, S. Hattori, Y. Takama, “Proposal of Personal-value-based Item Modeling and Its Application to Explanation of Recommendation,” TAAI2015, pp. 58-63, 2015.
[6] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” 1994 ACM Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994.
[7] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, Vol.7, No.1, pp. 76-80, 2003.
[8] R. Misawa, S.Hattori and Y. Takama, “Proposal of Extended Collaborative Filtering by Personal-value-based User Model,” The 28th Annual Conf. of the Japanese Society for Artificial Intelligence (JSAI2014), 1H4–NFC–01a–5, 2014 (in Japanese).
[9] J. L. Herlocker, J. A. Konstan, and J. Riedl, “Explaining collaborative filtering recommendations,” 2000 ACM Conf. on Computer Supported Cooperative Work, pp. 241-250, 2000.
[10] M. Bilgic and R. J. Mooney, “Explaining recommendations: Satisfaction vs. promotion,” Beyond Personalization 2005, pp. 13-18, 2005.
[11] R. J. Bayardo and R. Agrawal, “Mining the most interesting rules,” 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 145-154, 1999.
[12] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” 10th Int. Conf. on World Wide Web, pp. 285-295, 2001.
[13] Y.-J. Park, A. Tuzhilin, “The long tail of recommender systems and how to leverage it,” 2008 ACM Conf. on Recommender systems, pp. 11-18, 2008.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] S. Hattori and Y. Takama, “Recommender System Employing Personal-value-based User Model,” J. Adv. Comput. Intell. Intell. Inform. (JACIII), Vol.18, No.2, pp. 157-165, 2014.

[2] [2] A. I. Schein, A. Popescul, L. H. Ungar, and D. M. Pennock, “Methods and metrics for cold-start recommendations,” Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 253-260, 2002.

[3] [3] S. Lee, J. Yang, and S. Park, “Discovery of hidden similarity on collaborative filtering to overcome sparsity problem,” Discovery Science, Springer Berlin Heidelberg, Vol.3245, pp. 396-402, 2004.

[4] [4] N. Tintarev and J. Masthoff, “A survey of explanations in recommender systems,” IEEE 23rd Int. Conf. on Data Engineering Workshop, pp. 801-810, 2007.

[5] [5] T. Yamaguchi, S. Hattori, Y. Takama, “Proposal of Personal-value-based Item Modeling and Its Application to Explanation of Recommendation,” TAAI2015, pp. 58-63, 2015.

[6] [6] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: an open architecture for collaborative filtering of netnews,” 1994 ACM Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994.

[7] [7] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: Item-to-item collaborative filtering,” IEEE Internet Computing, Vol.7, No.1, pp. 76-80, 2003.

[8] [8] R. Misawa, S.Hattori and Y. Takama, “Proposal of Extended Collaborative Filtering by Personal-value-based User Model,” The 28th Annual Conf. of the Japanese Society for Artificial Intelligence (JSAI2014), 1H4–NFC–01a–5, 2014 (in Japanese).

[9] [9] J. L. Herlocker, J. A. Konstan, and J. Riedl, “Explaining collaborative filtering recommendations,” 2000 ACM Conf. on Computer Supported Cooperative Work, pp. 241-250, 2000.

[10] [10] M. Bilgic and R. J. Mooney, “Explaining recommendations: Satisfaction vs. promotion,” Beyond Personalization 2005, pp. 13-18, 2005.

[11] [11] R. J. Bayardo and R. Agrawal, “Mining the most interesting rules,” 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 145-154, 1999.

[12] [12] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Item-based collaborative filtering recommendation algorithms,” 10th Int. Conf. on World Wide Web, pp. 285-295, 2001.

[13] [13] Y.-J. Park, A. Tuzhilin, “The long tail of recommender systems and how to leverage it,” 2008 ACM Conf. on Recommender systems, pp. 11-18, 2008.

Personal Values-Based Item Modeling and its Application to Recommendation with Explanation

Yasufumi Takama*, Takayuki Yamaguchi*, and Shunichi Hattori**

Yasufumi Takama^, Takayuki Yamaguchi^, and Shunichi Hattori^**