Sparse Matrix Prediction Filling in Collaborative Filtering
Zhaobin Liu, Hui Wang, Wenyu Qu, Weijiang Liu, Ruoyu Fan · 2009
Collaborative filtering is one of the most successful techniques that attempts to recommend items (such as music, movies, web sites) that are likely of interest to the people. However, Existing CF technique may work poorly due to the sparse attribute inherent to the rating data. In this paper, a new mechanism that combines the user-based rating and item attribute-based is presented. First, we use the inherent item attributes to construct Boolean matrix. Second, we propose a novel blank unrated element prediction approach to compute the similarity of items by comparing the Euclidean distance between two items. Case studies show that our approach contributes to predict the unrated blank data for sparse matrix. The filling-in accuracy is also acceptable and reasonable.