Collaborative Filtering Recommendation Algorithm Based on User's Historical Behavior

Collaborative filtering is an important direction of data mining, the traditional collaborative filtering recommendation algorithm is sensitive for by data sparsity and cold start control, so recommendation result is difficult to achieve the ideal. In order to improve the accuracy of collaborative filtering recommendation, a collaborative filtering recommendation algorithm based on user behavior prediction is proposed in this paper.Preference for resources is predicted of according to the user's historical behavior, label is used to describe users Preference for resources and establish feature vector, and secondly similarity of resources are computed according to the feature vector and achieve personalized recommendation, finally a number of classic data are adopted to carry out the simulation test to verify the superiority. The test results show that the proposed algorithm greatly reduces the error of collaborative filtering recommendation and improve the accuracy to overcome the limitations of traditional collaborative filtering algorithms, but also can accelerate the recommended speed, so it has higher practical value.