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Collaborative filtering methods suffer from a data sparsity problem, which indicates that the accuracy of recommendation decreases when the user-item matrix used in recommendation is sparse. To alleviate the data sparsity problem, researches on data imputation have been done. In particular, the zero-injection method, which finds uninteresting items and imputes zero values to those items for collaborative...
In this paper, we study the problem of retrieving a ranked list of top-N items to a target user in recommender systems. We first develop a novel preference model by distinguishing different rating patterns of users, and then apply it to existing collaborative filtering (CF) algorithms. Our preference model, which is inspired by a voting method, is well-suited for representing qualitative user preferences...
Recently, product review sites that contain user ratings and trust statements such as Epinions.com have become popular, and many attempts have been made to analyze user behavior on those sites. In this paper, we first define three different user groups for each user, i.e. Trustee, Trust or, and Irrelevance which describe different kinds of trust statements. Then we investigate the relationship between...
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