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One of the most important applications of data mining is personalized recommendation. User participation plays a vital role in personalized recommendation systems, especially those based on collaborative filtering techniques. A user can get high-quality recommendations only when both the user himself/herself and other users actively participate, i.e. providing sufficient rating data. However, due to the rating cost, e.g. the privacy loss, rational users tend to provide as few ratings as possible. There is a trade-off between the rating cost and the recommendation quality. In this chapter, 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 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.
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- User Participation Game in Collaborative Filtering
- Chapter 5
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