Using Case-Based Reasoning and Social Trust to Improve the Performance of Recommender System in E-Commerce
Yanhong Guo, Guishi Deng, Guangqian Zhang, Chunyu Luo · 2007
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.