Abstract:

According to the long tail theory,the items with fewer feedbacks do not necessarily contain less information than those with more feedbacks.In the traditional collaborative filtering algorithms,the influences from unpopular items are usually ignored in the process of the eventual recommendation.To address this problem,an improved collaborative filtering recommendation model is proposed.By evaluating the unpopular items analytically,the weight of these items should be improved in calculating users’ similarities,so as to reflect users’ personalities and interests. Moreover,taking into account the impact of the time dependence,the time factor is introduced during the prediction of interests.Experimental results show that the algorithm can raise the accuracy of searching the nearest neighbors,and improve the recommendation quality of the collaborative filtering. 

Key words: feedback frequency;collaborative filtering;personalized recommendation;time factor