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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References
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using Collaborative Filtering to Weave an Information Tapestry. Communications of the ACM 35(12), 61–70 (1992)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Sarwar, B.M., Karypis, G., Konstan, J.A., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the tenth international conference on World Wide Web, pp. 285–295 (2001)
Wang, J., Vries, A.P., Reinders, M.J.T.: Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion. In: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, Seattle, Washington, USA, August 6-11, pp. 501–508 (2006)
Sarwar, B., Karpis, G., Konstan, J., Reidl, J.: Item-based Collaborative Filtering Recommendation Algorithms. In: Proceedings of the Tenth International World Wide Web Conference on world Wide Web (2001)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
de Campos, L.M., Fernndez-Luna, J.M., Huete, J.F.: A collaborative recommender system based on probabilistic inference from fuzzy observations. Fuzzy Sets and Systems 159(12), 1554–1576 (2008)
Hwang, C.-S., Chen, Y.-P.: Fuzzy Collaborative Filtering for Web Page Prediction. In: Proceedings of the 2006 Joint Conference on Information Sciences, JCIS 2006, Kaohsiung, Taiwan, October 8-11 (2006)
Castellano, G., Fanelli, A.M., Torsello, M.A.: A neuro-fuzzy collaborative filtering approach for web recommendation. International Journal of Computational Science 1(1), 27–29 (2007)
He, X.: Semantic distance and fuzzy users view in fuzzy databases. Journal of Computer Science and Technology 12(10), 757–764 (1989)
Munda, G., Nijkamp, P., Rietveld, P.: Comparison of fuzzy sets: a new semantic distance. Series Research Memoranda 0055, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics1 (1992)
Breese John, S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of UAI 1998, pp. 43–52 (1998)
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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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