Collaborative Filtering Recommendation Algorithm Based on MDP Model

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 recommendation´s 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.