Improved collaborative filtering algorithm based on symbolic data analysis

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.