期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 3, 页码 7114-7122出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.08.071
关键词
Recommendation system; Collaborative filtering; Source credibility; Importance weight; Neighbor selection
Collaborative filtering (CF) is the most commonly applied recommendation system for personalized services. Since CF systems rely on neighbors as information sources, the recommendation quality of CF depends on the recommenders selected. However, conventional CF has some fundamental limitations in selecting neighbors: recommender reliability proof, theoretical lack of credibility attributes, and no consideration of customers' heterogeneous characteristics. This study employs a multidimensional credibility model, source credibility from consumer psychology, and provides a theoretical background for credible neighbor selection. The proposed method extracts each consumer's importance weights on credibility attributes, which improves the recommendation performance by personalizing recommendations. (C) 2008 Elsevier Ltd. All rights reserved.
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