Recommendation algorithm based on item quality and user rating preferences

作     者：Yuan GUAN Shimin CAI Mingsheng SHANG 

作者机构：Web Sciences Center School of Computer Science and EngineeringUniversity of Electronic Science and Technology of China Chengdu 611731 China 

出 版 物：《Frontiers of Computer Science》 (中国计算机科学前沿（英文版）)

年 卷 期：2014年第8卷第2期

页      面：289-297页

核心收录：

学科分类：120202[管理学-企业管理（含：财务管理、市场营销、人力资源管理）] 12[管理学] 1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：国家自然科学基金 the Sichuan Provincial Science and Technology Department SMC specially appreciates the Fundamental Research Funds for the Center Universities 

主　　题：recommendation algorithm item quality userrating preferences RMSE 

摘      要：Recommender systems are one of the most im- portant technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filter- ing CF family, have problems ness. These problems hinder such as scalability and sparse- further developments of rec- ommender systems. We propose a new recommendation al- gorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accu- racy in rating prediction compared with the traditional ap- proaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.