Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 4, Pages 1339-1352Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2800731
Keywords
Behavior rule; collaborative filtering (CF); game theory; satisfaction equilibrium (SE)
Categories
Funding
- National Natural Science Foundation of China [61571300, 61371079, 61672137, 61602090]
- Young Elite Scientists Sponsorship Program By CAST [2016QNRC001]
- 111 Project [B17008]
- Thousand Youth Talents Program of China
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Collaborative filtering is widely used in recommendation systems. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e., provide sufficient ratings. However, due to the rating cost, rational users tend to provide as few ratings as possible. Therefore, there exists a tradeoff between the rating cost and the recommendation quality. In this paper, we model the interactions among users as a game in satisfaction form and study the corresponding equilibrium, namely satisfaction equilibrium (SE). Considering that accumulated ratings are used for generating recommendations, we design a behavior rule which allows users to achieve an SE via iteratively rating items. We theoretically analyze under what conditions an SE can be learned via the behavior rule. Experimental results on Jester and MovieLens data sets confirm the analysis and demonstrate that, if all users have moderate expectations for recommendation quality and satisfied users are willing to provide more ratings, then all users can get satisfying recommendations without providing many ratings. The SE analysis of the proposed game in this paper is helpful for designing mechanisms to encourage user participation.
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