Novel personal and group-based trust models in collaborative filtering for document recommendation

  • Authors:

  • Chin-Hui Lai;Duen-Ren Liu;Cai-Sin Lin

  • Affiliations:

  • Department of Information Management, Chung Yuan Christian University, Taiwan;Institute of Information Management, National Chiao Tung University, Taiwan;Institute of Information Management, National Chiao Tung University, Taiwan

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2013

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Abstract

Collaborative filtering (CF) recommender systems have been used in various application domains to solve the information-overload problem. Recently, trust-based recommender systems have incorporated the trustworthiness of users into CF techniques in order to improve recommendation quality. Some researchers have proposed rating-based trust models to derive trust values based on users' past ratings of items, or based on explicitly specified relations (e.g. friends) or trust relationships; however, the rating-based trust model may not be effective in CF recommendations due to unreliable trust values derived from very few past rating records. In this work, we propose a hybrid personal trust model which adaptively combines the rating-based trust model and explicit trust metric to resolve the drawback caused by insufficient past rating records. Moreover, users with similar preferences usually form a group to share items (knowledge) with each other; thus, users' preferences may be affected by group members. Accordingly, group trust can enhance personal trust to support recommendations from the group perspective. We then propose a recommendation method based on a hybrid model of personal and group trust to improve recommendation performance. The experimental results show that the proposed models can improve the prediction accuracy of other trust-based recommender systems.