标题：A novel rating style mining method to improve collaborative filtering algorithm
作者：Yang, Wei; Guo, Sheng Hui; Zhang, Chun Jin
作者机构：[Yang, Wei; Guo, Sheng Hui] Shandong Univ Sci & Technol, Key Lab Wisdom Mine Informat Technol Shandong Pro, Qingdao, Shandong, Peoples R China.; [Zh 更多
会议名称：International Symposium on Power Electronics and Control Engineering (ISPECE)
会议日期：DEC 28-30, 2018
来源：2018 INTERNATIONAL SYMPOSIUM ON POWER ELECTRONICS AND CONTROL ENGINEERING (ISPECE 2018)
摘要：Collaborative filtering (CF) algorithm is widely used in recommendation systems, which makes recommendation based on the neighbors' interests. Therefore, how to discover the neighbors with similar interests to target user is the core of the CF algorithm. Most existing algorithms discover neighbors by using rating similarity measure, which ignore the differences of users' rating styles. In this paper, we propose a user rating style mining method and use it to eliminate the rating style differences before calculating a similarity measure. Comparing with the raw similarity measure and another rating style mining method with the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) over amazon movie dataset, we conclude that (i) use our method to eliminate the rating styles differences can improve the prediction accuracy and (ii) our method outperforms other rating style mining method.