Hybrid Collaborative Filtering Recommendation Algorithm Based on Model Filling
Shiqing Wang · Microcomputer Information · 2011
As the Electronic data of goods and services expanding every day, collaborative filtering (CF) has become a popular and attractive technique in recommender systems. In this paper, a hybrid approach is proposed to solve problems which are challenges of the collaborative filtering, such as data sparsity, accurate of similarities. The experimental results show that our hybrid method can efficiently improve the extreme sparsity of user rating data, and improve the accurate of similarities in some extent.