A New Prediction Approach Based on Linear Regression for Collaborative Filtering
Recommender systems using collaborative filtering help users filter information based on previous knowledge of users preferences. Most of existing recommender systems make predictions using weighted average method. This paper introduces a new prediction approach based on an effective linear regression model. One fundamental idea behind this approach is that there exist patterns among different users preferences. And we propose a linear regression model to characterize the inner relationships among different users rating habits. The major contribution of this approach is that it can make more accurate predictions via utilizing the exact linear correlation indicated by Pearson Correlation Coefficient directly. The preliminary experiments show that our approach can improve the accuracy of prediction thus make recommendations more appealing to users.
Collaborative Filtering Recommender System Prediction Linear Regression
Xinyang Ge Jia Liu Qi Qi Zhenyu Chen
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China Software Institute, Nanjing University, Nanjing, China
国际会议
上海
英文
2646-2650
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）