Abstract
The problem of building recommender systems has attracted considerable attention in recent years. Collaborative Filtering (CF) is one of the most successful and widely used approaches in recommend system. Traditional collaborative filtering requires explicit user participation for providing his/her interest to the items. In this paper, we propose a novel collaborative filtering approach based on the fuzzy set theory, in which we originally introduced the fuzzy set and semantic distance metric to improve the sharp boundary problem of rating values fundamentally. The experimental results demonstrate that the proposed methods can solve the sharp boundary problem of rating items and achieve a much more desirable performance than the traditional CF.
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Li, Jh., Li, Xs., Liu, Hl., Han, Xj., Zhang, J. (2009). Fuzzy Collaborative Filtering Approach Based on Semantic Distance. In: Cao, B., Li, TF., Zhang, CY. (eds) Fuzzy Information and Engineering Volume 2. Advances in Intelligent and Soft Computing, vol 62. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03664-4_21
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DOI: https://doi.org/10.1007/978-3-642-03664-4_21
Publisher Name: Springer, Berlin, Heidelberg
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