关键词: 推荐系统, 协同过滤, 网络数据挖掘, 径向基函数

Abstract: Collaborative filtering is the most successful method in recommender system, but it has cold start, sparsity, scalability and other issues when faced with sparse data. Web Usage Mining（WUM） is proposed to obtain implicit data which can fill explicit user rating matrix, and Radial Basis Function Network（RBFN） is used to smoothen filled rating matrix to get a complete rating matrix. Collaborative filtering is used to classify similar users based on smoothed complete rating matrix and generate recommendation. Experimental results show that compared with traditional collaborative filtering methods, the proposed method is more effective both in the accuracy or relevance of recommendations.

Key words: recommender system, collaborative filtering, Web Usage Mining, radial basis function network