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Using Case-Based Reasoning and Social Trust to Improve the Performance of Recommender System in E-Commerce

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4 Author(s)
Guo, YanHong ; Dalian Univ. of Technol., Dalian ; Guishi Deng ; Guangqian Zhang ; Chunyu Luo

Collaborative filtering recommender systems have become important tools of making personalized recommendations for products or services in E-commerce nowadays. In fact, case-based reasoning has some natural similarity with collaborative filtering from the view of recognizing science. This paper proposes a novel idea of combing CBR and CF algorithm together to improve the performance of recommender systems. For another, a social trust model is advanced in the recommendation steps to improve the prediction accuracy. Experimental results show that using case-based reasoning and social trust have better prediction results and solve the sparsity problem of recommender systems from certain angle.

Published in:

Innovative Computing, Information and Control, 2007. ICICIC '07. Second International Conference on

Date of Conference:

5-7 Sept. 2007