关键词: 协同过滤, 符号数据分析, 奇异值分解, 稀疏性, 推荐系统

Abstract: With the continuing increase of users and kinds of resources, the problem of rating matrix's sparsity is becoming more and more prominent, which seriously affects the quality of the recommendation system. Singular Value Decomposition (SVD) is a dimension reduction method, and Symbolic Data Analysis (SDA) is a new analytical approach to processing mass data. This paper proposed a new collaborative filtering recommendation algorithm which combines SVD with SDA. The experimental results based on EachMovie database set indicate that the proposed method is significantly better than traditional general recommendation algorithm when the data is particularly sparse.

Key words: collaborative filtering, Symbolic Data Analysis (SDA), Singular Value Decomposition (SVD), sparsity, recommendation system