A New Hybrid Collaborative Filtering Algorithm with Combination of Trust

Abstract

Trust as one of additional information has been merged into traditional collaborative filtering(CF) to alleviate data sparisity and cold start problems. However, the trust based CF, some predict ratings only based on the ratings of trusted neighbors, or some rely on CF to find similar users before they can apply trust information, and very few of them merge user-based and item-based approaches together, but usually depend on heterogeneous information. In this paper, we propose a novel method called “Hybridâ€. First, before CF we pre-process the user profile by using the ratings of trusted neighbors to form a new profile, which covers more ratings both of the active user and trusted users. Then, we use a novel general hybrid CF which merges user-based and item-based approaches together only based on rating information for making recommendations. Experimental results demonstrate that our method outperforms others both in accuracy and coverage.