Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation

Yasufumi Takama; Yu-Sheng Chen; Ryori Misawa; Hiroshi Ishikawa

doi:10.20965/jaciii.2018.p0506

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JACIII Vol.22 No.4 pp. 506-513

doi: 10.20965/jaciii.2018.p0506

(2018)

Paper:

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Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation

Yasufumi Takama, Yu-Sheng Chen, Ryori Misawa, and Hiroshi Ishikawa

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

Received:

February 19, 2018

Accepted:

April 23, 2018

Published:

July 20, 2018

Keywords:

recommender systems, personal values, long-tail

Abstract

This paper examines the potential of personal values-based user modeling for long tail item recommendation. Long tail items are defined as those which are not popular but are preferred by small numbers of specific users. Although recommending long tail items to relevant users is beneficial for both the providers and consumers of such items, it is known to be a challenge for most recommendation algorithms. In particular, a long tail item is one that would be purchased and/or rated by a small number of users, so it is difficult to predict its rating accurately. This paper assumes that the influence of personal values becomes more obvious when users evaluate long tail items, and examines it through offline experiment. The Rating Matching Rate (RMRate) has been proposed in order to incorporate users’ personal values into recommender systems. As the RMRate models personal values as the weight of an item’s attribute, it is easy to incorporate into existing recommendation algorithms. An experiment was conducted to evaluate the performance of long tail item recommendation; Experimental result shows that personal values-based user modeling can recommend less popular items while maintaining precision.

Long-tail structure of dataset from 4travel.

Cite this article as:

Y. Takama, Y. 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.

Data files:

References

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[2] C. Anderson, “The Long Tail: Why the Future of Business is Selling Less of More,” Hyperion, 2006.
[3] Y.-J. Park and A. Tuzhilin, “The long tail of recommender systems and how to leverage it,” Proc. of the 2008 ACM Conf. on Recommender Systems, pp. 11-18, 2008.
[4] H. Yin et al., “Challenging the long tail recommendation,” Proc. of the VLDB Endowment, Vol.5, No.9, pp. 896-907, 2012.
[5] T. Hwang and Y. Li, “Optimal Recommendation and Long-tail Provision Strategies for Content Monetization,” 7th Hawaii Int. Conf. on System Science, pp. 1316-1323, 2014.
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[8] A. I. Schein, “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.
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[14] M. Rokeach, “The Nature of Human Values,” The Free Press, 1973.
[15] P. Resnick et al., “GroupLens: an open architecture for collaborative filtering of netnews,” Proc. of ACM 1994 Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994.
[16] Y. Takama et al., “Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation,” IWACIII2017, AS1-3-2, 2017.
[17] D. E. Vinson, J. E. Scott, and L. M. Lamont, “The role of personal values in marketing and consumer behavior,” J. of Marketing, pp. 44-50, 1977.
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[19] C. Jayawardhena, “Personal values’ influence on e-shopping attitude and behaviour,” Internet Research, Vol.14, No.2, pp. 127-138, 2004.
[20] W. Wu, L. Chen, and L. He, “Using Personality to Adjust Diversity in Recommender Systems,” 24th ACM Conf. on Hypertext and Social Media, pp. 225-229, 2013.
[21] L. Shi, “Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach,” Proc. of the 7th ACM Conf. on Recommender Systems, pp. 57-64, 2013.
[22] 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.
[23] M. A. Ghazanfar and A. Prugel-Bennett, “An Improved Switching Hybrid Recommender System Using Naive Bayes Classifier and Collaborative Filtering,” Proc. of The Int. Multi Conf. of Engineers and Computer Scientists, Vol.1, pp. 493-502, 2010.
[24] Ó. Celma, “Music Recommendation and Discovery in the Long Tail,” Ph.D. thesis, Universitat Pompeu Fabra, 2008.
[25] U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating “Word of Mouth”,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 210-217, 1995.
[26] P. Cremonesi, Y. Koren, and R. Turrin, “Performance of Recommender Algorithms on Top-N Recommendation Tasks,” RecSys’10, Proc. of the 4th ACM Conf. on Recommender Systems, pp. 39-46, 2010.

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

[1] [1] H. Ishikawa, “Social Big Data Mining,” CRC Press, 2015.

[2] [2] C. Anderson, “The Long Tail: Why the Future of Business is Selling Less of More,” Hyperion, 2006.

[3] [3] Y.-J. Park and A. Tuzhilin, “The long tail of recommender systems and how to leverage it,” Proc. of the 2008 ACM Conf. on Recommender Systems, pp. 11-18, 2008.

[4] [4] H. Yin et al., “Challenging the long tail recommendation,” Proc. of the VLDB Endowment, Vol.5, No.9, pp. 896-907, 2012.

[5] [5] T. Hwang and Y. Li, “Optimal Recommendation and Long-tail Provision Strategies for Content Monetization,” 7th Hawaii Int. Conf. on System Science, pp. 1316-1323, 2014.

[6] [6] M. D. Ekstrand et al., “User perception of differences in recommender algorithms,” RecSys’14, Proc. of the 8th ACM Conf. on Recommender Systems, pp. 161-168, 2014.

[7] [7] S. Lee, J. Yang, and S.-Y. Park, “Discovery of hidden similarity on collaborative filtering to overcome sparsity problem,” Discovery Science, Springer, pp. 396-402, 2004.

[8] [8] A. I. Schein, “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.

[9] [9] J. Bobadilla et al., “A collaborative filtering approach to mitigate the new user cold start problem,” Knowledge-Based Systems, Vol.26, pp. 225-238, 2012.

[10] [10] S.-T. Park et al., “Naïve Filterbots for Robust Cold-start Recommendations,” KDD’06, Proc. of the 12th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 699-705, 2006.

[11] [11] P. Massa and P. Avesani, “Trust-aware Collaborative Filtering for Recommender Systems,” Lecture Notes in Computer Science, Vol.3290, pp. 492-508, 2004.

[12] [12] M. A. S. N. Nunes and R. Hu, “Personality-based recommender systems: an overview,” RecSys’12, Proc. of the 6th ACM Conf. on Recommender Systems, pp. 5-6, 2012.

[13] [13] 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.

[14] [14] M. Rokeach, “The Nature of Human Values,” The Free Press, 1973.

[15] [15] P. Resnick et al., “GroupLens: an open architecture for collaborative filtering of netnews,” Proc. of ACM 1994 Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994.

[16] [16] Y. Takama et al., “Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation,” IWACIII2017, AS1-3-2, 2017.

[17] [17] D. E. Vinson, J. E. Scott, and L. M. Lamont, “The role of personal values in marketing and consumer behavior,” J. of Marketing, pp. 44-50, 1977.

[18] [18] M. B. Holbrook, “Consumer value: a framework for analysis and research,” Psychology Press, 1999.

[19] [19] C. Jayawardhena, “Personal values’ influence on e-shopping attitude and behaviour,” Internet Research, Vol.14, No.2, pp. 127-138, 2004.

[20] [20] W. Wu, L. Chen, and L. He, “Using Personality to Adjust Diversity in Recommender Systems,” 24th ACM Conf. on Hypertext and Social Media, pp. 225-229, 2013.

[21] [21] L. Shi, “Trading-off among accuracy, similarity, diversity, and long-tail: a graph-based recommendation approach,” Proc. of the 7th ACM Conf. on Recommender Systems, pp. 57-64, 2013.

[22] [22] 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.

[23] [23] M. A. Ghazanfar and A. Prugel-Bennett, “An Improved Switching Hybrid Recommender System Using Naive Bayes Classifier and Collaborative Filtering,” Proc. of The Int. Multi Conf. of Engineers and Computer Scientists, Vol.1, pp. 493-502, 2010.

[24] [24] Ó. Celma, “Music Recommendation and Discovery in the Long Tail,” Ph.D. thesis, Universitat Pompeu Fabra, 2008.

[25] [25] U. Shardanand and P. Maes, “Social Information Filtering: Algorithms for Automating “Word of Mouth”,” Proc. of the SIGCHI Conf. on Human Factors in Computing Systems, pp. 210-217, 1995.

[26] [26] P. Cremonesi, Y. Koren, and R. Turrin, “Performance of Recommender Algorithms on Top-N Recommendation Tasks,” RecSys’10, Proc. of the 4th ACM Conf. on Recommender Systems, pp. 39-46, 2010.