An Electronic Commerce Recommendation Algorithm Joining Case-Based Reasoning and Collaborative Filtering

Abstract

With the rapid development of network, information technology has provided an unprecedented amount of information resources. It has also led to the problem of information overload. Electronic commerce personalized recommender systems represent services that aim at predicting a customer's interest on information products available in the application domain, using customers' ratings on products. Peoples' experiences often do not enough to deal with the vast amount of available information. Thus, methods to help find products of electronic commerce have attracted much attention from both researchers and vendors. Collaborative filtering technology has proved to be one of the most effective for its simplicity in both theory and implementation. The paper gives an electronic commerce recommendation algorithm combining case-based reasoning and collaborative filtering. Firstly, it uses case-based reasoning to fill the vacant ratings. Then, it produces prediction collaborative filtering. The presented algorithm combining case-based reasoning and collaborative filtering can alleviate the sparsity issue.