Game Theoretic Models for Personalized Recommendation System
One of the most promising recommendation technologies is collaborative filtering.However,the existing collaborative filtering methods do not consider the drifting of the users interests.For this reason,the systems may recommend unsatisfactory items when the users interests changed.In order to produce high quality of recommendation,a novel collaborative filtering recommendation algorithm is proposed in this paper,which can trace the users interests through studying the game process between recommendation systems and users.Firstly,according to the definition of mutual replaceable goods in economics,we give the definition of mutual replaceable objects collection which is presented the mutual replaceable relationship in items.Then we propose a recommendation algorithm based on the Dynamic Game Theory Mode1.Experimental results show that the proposed method can discover the change of user interest timely,and improve the system recommendation quality.
Quality management Performance Excellence Factor Analysis Clustering algorithm
Atiao Yang Yong Tang Jiangbin Wang Yuan Zhao
School of Computer Science,South China Normal University,Guangzhou,China;School of Mathematics and C School of Computer Science,South China Normal University,Guangzhou,China Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen,China Dept.of Basic Experiment,Naval Aeronautical and Astronautical University,Yantai,China
国际会议
南昌
英文
1-7
2013-09-26（万方平台首次上网日期，不代表论文的发表时间）