JACIII Vol.22 No.4 pp. 506-513
doi: 10.20965/jaciii.2018.p0506


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

February 19, 2018
April 23, 2018
July 20, 2018
recommender systems, personal values, long-tail
Analyzing Potential of Personal Values-Based User Modeling for Long Tail Item Recommendation

Long-tail structure of dataset from 4travel.

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.

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.
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Last updated on Aug. 20, 2018