Abstract: Recently,collaborative filtering is one of the most widely used and successful recommendation technology in the recommender system.However,the traditional collaborative filtering recommendation algorithm has the disadvantages of the one-sidedness for selecting neighbors and lower recommendation precision.In order to solve the problems,this paper proposed a collaborative filtering recommendation algorithm based on overlap degrees and double attributes.Firstly,we used the aggregated results of similarity and overlap degrees to select the recommended set of objects.Then,we proposed the concept of double attributes,and calculated the reliability of the target user and the popularity of the target item respectively.At last,taking into account the two groups,we used both user and item rating information to generate recommendation for the target user.Experimental results show the proposed algorithm is improved significantlyin terms of neighbor selection and recommendation quality compared to traditional collaborative filtering recommendation algorithm.

Key words: Overlap degrees,Recommender object,Double attributes,Collaborative filtering,Recommender systems