Collaborative filtering Recommendation Algorithm based on MDP model

摘要：

　　Collaborative filtering,which makes personalized predictions by learning the historical behaviors of users,is widely used in recommender systems.It makes the prediction and recommend by similarity of users,and it can handle the various work.But the traditional collaborative filtering ignores the connection of users and items.Affect the recommendations results.To find similar users by measuring the customer relationship between the neighbors can improve the accuracy of prediction user interests.Then it can improve the accuracy of the recommendation.So collaborative filtering recommendation algorithm based on MDP model is proposed.It can find the connection of users purchase and next purchase.So it can predict users next purchase.Then can recommend items to users.The test results shows the algorithm of this paper have more accuracy.

关键词： component Collaborative filtering MDP model

作者: Wang Xingang Li Chenghao

作者单位: School of Information Qilu University of Technology Jinan,China

会议类型: 国际会议

会议名称: The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

会议地点: 贵阳

会议语种:英文

页码: 110-113

在线出版日期: 2015-08-18（万方平台首次上网日期，不代表论文的发表时间）