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JACIII Vol.16 No.3 pp. 404-411
doi: 10.20965/jaciii.2012.p0404
(2012)

Paper:

Investigation About Applicability of Personal Values for Recommender System

Shunichi Hattori and Yasufumi Takama

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

Received:
September 15, 2011
Accepted:
December 8, 2011
Published:
May 20, 2012
Keywords:
information recommendation, user modeling, personal values
Abstract

A recommender systemis a fundamental technique for finding information that is likely to be preferred by users among vast amounts of information. While existing recommender systems usually employ user preference or attributes of items to make recommendations, marketing fields have been taking notice of personal values, because that such values are significantly related to user preference. This paper investigates the applicability of personal values in modeling items and users. The results of questionnaires show the feasibility of a recommender system based on personal values.

Cite this article as:
Shunichi Hattori and Yasufumi Takama, “Investigation About Applicability of Personal Values for Recommender System,” J. Adv. Comput. Intell. Intell. Inform., Vol.16, No.3, pp. 404-411, 2012.
Data files:
References
  1. [1] C. N. Ziegler, S.M.McNee, J. A. Konstan, and G. Lausen, “Improving Recommendation Lists Through Topic Diversification,” WWW ’05 Proc. of the 14th Int. Conf. on World Wide Web, pp. 22-32, 2005.
  2. [2] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. of ACM 1994 Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994.
  3. [3] 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.
  4. [4] C. N. Ziegler, G. Lausen, and L. S. Thieme, “Taxonomy-driven Computation of Product Recommendations,” Proc. of the 13th ACM Int. Conf. on Information and knowledge management, pp. 406-415, 2004.
  5. [5] Y. Hijikata, T. Shimizu, and S. Nishida, “Discovery-oriented Collaborative Filtering for Improving User Satisfaction,” Proc. of 14th ACM Int. Conf. on Intelligent User Interfaces, pp. 67-76, 2009.
  6. [6] T. Akiyama, K. Obara, and M. Tanizaki, “Proposal and Evaluation of Serendipitous Recommendation Method Using General Unexpectedness,” ACM PRSAT Workshop on Recommender Systems, 2010.
  7. [7] M. Rokeach, “Beliefs, Attitudes, and Values,” San Francisco: Jossey Bass, p. 161, 1968.
  8. [8] M. Rokeach, “The Nature of Human Values,” New York: The Free Press, 1973.
  9. [9] D. E. Vinson, J. E. Scott, and L. M. Lamont, “The role of personal values in marketing and consumer behavior,” The J. of Marketing, Vol.41, No.2, pp. 44-50, 1977.
  10. [10] M. B. Holbrook, “Consumer value: a framework for analysis and research,” Routledge, 1999.
  11. [11] C. E. Osgood, C. J. Suci, and P. H. Tannenbaum, “TheMeasurement of Meaning,” University of Illinois Press, pp. 76-124, 1957.
  12. [12] J. C. Bezdek, “Pattern Recognition with Fuzzy Objective Function Algorithms,” Plenum Press, 1981.

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