An Improved Collaborative Filtering Model Considering Item Similarity
Yefei Zha, Yuqing Zhai · 2013
Because of its simplicity and effectiveness, collaborative filtering (CF) became one of the most successful recommendation algorithms. User-based CF is one classic method of CF algorithms. In order to solve the problem that common rating items are often too few to be used to effectively calculate the similarity of two users in user-based CF, we proposed an improved collaborative filtering model with item similarity called ISCF in this paper. In ISCF model, the similarity of items was considered in user-based collaborative filtering, which contributes to alleviate the problem of data sparsity and therefore calculate the similarity of user. Experimental results illustrate that our approach ISCF outperforms the average method and user-based CF. Compared with user-based CF, the average improvement in the percentage of ISCF at MAE and RMSE are 21.9% and 17.7%, respectively. In addition, our approach ISCF can predict more items than user-based CF, and the average improvement in the percentage of ISCF at prediction diversity is 33.86%.