An Electronic Commerce Collaborative Filtering Recommedation Algorithm Based on User Context
Peng Fang · Applied Mechanics and Materials · 2014
With the speedy development of Internet, information technology has provided an unmatched amount of information resources. To help people to find helpful information, electronic commerce personalized recommendation technique emerges. Collaborative filtering is one successful personalized recommendation technology, and is widely used in many fields. But traditional collaborative filtering recommendation algorithm has the problem of sparsity, which will influence the efficiency of prediction. User context information is rarely considered in the recommendation process, especially in the collaborative filtering. In this paper, a new electronic commerce collaborative filtering recommendation algorithm is given which applies the user context information. This method combines the rating similarity and the user context similarity in the electronic commerce recommendation process to improve the prediction accuracy by efficiently managing the problem of data sparsity.