Integrating Multi-Criteria Collaborative Filtering and Trust filtering for personalized Recommender Systems

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
IEEE SSCI 2011 - Symposium Series on Computational Intelligence - MCDM 2011: 2011 IEEE Symposium on Computational Intelligence in Multicriteria Decision-Making, 2011, pp. 44 - 51
Issue Date:
2011-07-12
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Recommender Systems are information systems that attempt to recommend items of interest to particular users based on their explicit and implicit preferences. Multi-Criteria Decision Making (MCDM) aims at assisting the decision maker in the decision making process, or giving the decision maker a recommendation, concerning a set of actions, alternatives, items etc. Thus, despite their differences, Recommender Systems and Multi-Criteria Decision Making share the same objective which is supporting the decision making process and reducing information overload. In this paper we propose a novel hybrid Multi-Criteria Trust-enhanced CF (MC-TeCF) approach. The proposed MC-TeCF approach combines the MC user-based CF and the MC user-based Trust filtering approaches to alleviate the standard Single-Criteria user-based CF limitations. Empirical results demonstrate the significance and effectiveness of the proposed MC-TeCF approach in terms of improving accuracy, as well as in dealing with very sparse data sets or cold start users compared with the standard Single-Criteria user-based CF approach. © 2011 IEEE.
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