Journal
DECISION SUPPORT SYSTEMS
Volume 54, Issue 1, Pages 768-780Publisher
ELSEVIER
DOI: 10.1016/j.dss.2012.09.005
Keywords
Recommender systems; Collaborative filtering; Trust filtering; Semantic filtering; Information fusion; Cold-start; Data sparsity
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Collaborative Filtering (CF) is the most popular recommendation technique but still suffers from data sparsity, user and item cold-start problems, resulting in poor recommendation accuracy and reduced coverage. This study incorporates additional information from the users social trust network and the items' semantic domain knowledge to alleviate these problems. It proposes an innovative Trust-Semantic Fusion (TSF)-based recommendation approach within the CF framework. Experiments demonstrate that the TSF approach significantly outperforms existing recommendation algorithms in terms of recommendation accuracy and coverage when dealing with the above problems. A business-to-business recommender system case study validates the applicability of the TSF approach. (C) 2012 Elsevier B.V. All rights reserved.
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