Multidimensional credibility model for neighbor selection in collaborative recommendation

摘要: Collaborative filtering (CF) is the most commonly applied recommendation system for personalized services. Since CF systems rely on neighbors as information sources, quality of depends recommenders selected. However, conventional has some fundamental limitations in selecting neighbors: recommender reliability proof, theoretical lack credibility attributes, and no consideration customers' heterogeneous characteristics. This study employs a multidimensional model, source from consumer psychology, provides background credible neighbor selection. The proposed method extracts each consumer's importance weights which improves performance by personalizing recommendations.