Title: Novel personal and group-based trust models in collaborative filtering for document recommendation
Authors: Lai, Chin-Hui
Liu, Duen-Ren
Lin, Cai-Sin
資訊管理與財務金融系 註:原資管所+財金所
Department of Information Management and Finance
Keywords: Collaborative filtering;Trust-based recommender system;Document recommendation;Role relationship
Issue Date: 1-Aug-2013
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. (C) 2013 Elsevier Inc. All rights reserved.
URI: http://dx.doi.org/10.1016/j.ins.2013.03.030
http://hdl.handle.net/11536/22174
ISSN: 0020-0255
DOI: 10.1016/j.ins.2013.03.030
Journal: INFORMATION SCIENCES
Volume: 239
Issue: 
Begin Page: 31
End Page: 49
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