Effective collaborative filtering approaches based on missing data imputation

Recommender system emerges as a technology addressing "information overload" problem. Collaborative Filtering (CF) is successful and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper is with an eye to missing data imputation strategies in nearest-neighbor CF. We propose two novel effective CF approaches based on missing data imputation, which utilizes user's demographic information before conducting CF process. In the experiments, user's age range and occupation information are employed in the imputation stage. The results show that the proposed approaches effectively smooth the sparsity of rating data, and perform better prediction than traditional widely-used CF algorithms.