Recommender System Employing Personal-Value-Based User Model

Shunichi Hattori; Yasufumi Takama

doi:10.20965/jaciii.2014.p0157

single-jc.php

« previous

JACIII Vol.18 No.2 pp. 157-165

doi: 10.20965/jaciii.2014.p0157

(2014)

Paper:

Views over last 60 days: 809

Recommender System Employing Personal-Value-Based User Model

Shunichi Hattori and Yasufumi Takama

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

Received:

September 11, 2013

Accepted:

January 27, 2014

Published:

March 20, 2014

Keywords:

recommender system, personal values, user model, cold-start problem

Abstract

This paper proposes a recommender system based on personal-value-based user model. Conventional methods such as collaborative and content-based approaches tend to be less accurate for new users and items due to the lack of a relation between items and user preferences. While existing recommender systems usually employ user preferences of items for making recommendations, proposed method focuses on users’ personal values, which mean value judgments regarding on which attributes users put a high priority. The proposed recommender system employing personal-value-based user model is thus expected to realize more precise recommendations in cold-start situations. As one of typical cold-start situations, a prototype system is developed for recommendation using external resources. Experimental results show that generated user models reflect each user’s value judgment on attributes. In addition, the results also show that recommendation employing the proposed user model realizes improvements of precision in cold-start situations.

Cite this article as:

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.

Data files:

References

[1] P. Resnick et al., “GroupLens: an open architecture for collaborative filtering of netnews,” In Proc. of the 1994 ACM Conf. on Computer Supported Cooperative Work, pp. 175.186, 1994.
[2] F. Pachet, P. Roy, and D. Cazaly, “A combinatorial approach to content-based music selection,” Multimedia, IEEE, Vol.7, No.1, pp. 44-51, 2000.
[3] A. I. Schein et al., “Methods and metrics for cold-start recommendations,” In Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 253-260, 2002.
[4] S. Hattori and Y. Takama, “Proposal of User Modeling Method Employing Reputation Analysis on User Reviews Based on Personal Values,” In the 27th Annual Conf. of Japanese Society for Artificial Intelligence, 1A3-IOS-3a-4, 2013.
[5] S. Lee, J. Yang, and S.-Y. Park, “Discovery of hidden similarity on collaborative filtering to overcome sparsity problem,” In Discovery Science, pp. 396-402. Springer, 2004.
[6] J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” In Proc. of the 14th Conf. on Uncertainty in Artificial Intelligence, pp. 43-52, 1998.
[7] S.-T. Park et al., “Naïve filterbots for robust cold-start recommendations,” In Proc. of the 12th ACM SIGKDD Int. Conf. on Knowledge discovery and Data Mining, pp. 699-705, 2006.
[8] H. Yildirim and M. S. Krishnamoorthy, “A random walk method for alleviating the sparsity problem in collaborative filtering,” In Proc. of the 2008 ACM Conf. on Recommender Systems, pp. 131-138, 2008.
[9] I. Fernández-Tobías et al., “A generic semantic-based framework for cross-domain recommendation,” In Proc. of the 2nd Int. Workshop on Information Heterogeneity and Fusion in Recommender Systems, Het-Rec 11, pp. 25-32, ACM, New York, USA, 2011.
[10] V. C. Ostuni et al., “Top-N recommendations from implicit feedback leveraging linked opendata,” In Proc. of the 7th ACM Conf. on Recommender systems (RecSys 13), pp. 85-92, ACM, New York, USA, 2013.
[11] M. Rokeach, “The Nature of Human Values,” Vol.70, Free Press, 1973.
[12] D. E. Vinson, J. E. Scott, and L. M. Lamont, “The role of personal values in marketing and consumer behavior,” The J. of Marketing, pp. 44-50, 1977.
[13] M. B. Holbrook, “Consumer value: a framework for analysis and research,” Psychology Press, 1999.
[14] C. Jayawardhena, “Personal values’ influence on e-shopping attitude and behaviour,” Internet Research, Vol.14, No.2, pp. 127-138, 2004.
[15] S. Hattori and Y. Takama, “Investigation About Applicability of Personal Values for Recommender System,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.16, No.3, pp. 404-411, 2012.
[16] G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. on Knowl. and Data Eng., Vol.17, No.6, pp. 734-749, June 2005.

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

[1] [1] P. Resnick et al., “GroupLens: an open architecture for collaborative filtering of netnews,” In Proc. of the 1994 ACM Conf. on Computer Supported Cooperative Work, pp. 175.186, 1994.

[2] [2] F. Pachet, P. Roy, and D. Cazaly, “A combinatorial approach to content-based music selection,” Multimedia, IEEE, Vol.7, No.1, pp. 44-51, 2000.

[3] [3] A. I. Schein et al., “Methods and metrics for cold-start recommendations,” In Proc. of the 25th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 253-260, 2002.

[4] [4] S. Hattori and Y. Takama, “Proposal of User Modeling Method Employing Reputation Analysis on User Reviews Based on Personal Values,” In the 27th Annual Conf. of Japanese Society for Artificial Intelligence, 1A3-IOS-3a-4, 2013.

[5] [5] S. Lee, J. Yang, and S.-Y. Park, “Discovery of hidden similarity on collaborative filtering to overcome sparsity problem,” In Discovery Science, pp. 396-402. Springer, 2004.

[6] [6] J. S. Breese, D. Heckerman, and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering,” In Proc. of the 14th Conf. on Uncertainty in Artificial Intelligence, pp. 43-52, 1998.

[7] [7] S.-T. Park et al., “Naïve filterbots for robust cold-start recommendations,” In Proc. of the 12th ACM SIGKDD Int. Conf. on Knowledge discovery and Data Mining, pp. 699-705, 2006.

[8] [8] H. Yildirim and M. S. Krishnamoorthy, “A random walk method for alleviating the sparsity problem in collaborative filtering,” In Proc. of the 2008 ACM Conf. on Recommender Systems, pp. 131-138, 2008.

[9] [9] I. Fernández-Tobías et al., “A generic semantic-based framework for cross-domain recommendation,” In Proc. of the 2nd Int. Workshop on Information Heterogeneity and Fusion in Recommender Systems, Het-Rec 11, pp. 25-32, ACM, New York, USA, 2011.

[10] [10] V. C. Ostuni et al., “Top-N recommendations from implicit feedback leveraging linked opendata,” In Proc. of the 7th ACM Conf. on Recommender systems (RecSys 13), pp. 85-92, ACM, New York, USA, 2013.

[11] [11] M. Rokeach, “The Nature of Human Values,” Vol.70, Free Press, 1973.

[12] [12] D. E. Vinson, J. E. Scott, and L. M. Lamont, “The role of personal values in marketing and consumer behavior,” The J. of Marketing, pp. 44-50, 1977.

[13] [13] M. B. Holbrook, “Consumer value: a framework for analysis and research,” Psychology Press, 1999.

[14] [14] C. Jayawardhena, “Personal values’ influence on e-shopping attitude and behaviour,” Internet Research, Vol.14, No.2, pp. 127-138, 2004.

[15] [15] S. Hattori and Y. Takama, “Investigation About Applicability of Personal Values for Recommender System,” J. of Advanced Computational Intelligence and Intelligent Informatics, Vol.16, No.3, pp. 404-411, 2012.

[16] [16] G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions,” IEEE Trans. on Knowl. and Data Eng., Vol.17, No.6, pp. 734-749, June 2005.