Abstract: Collaborative filtering is one of the most widely used techniques for recommendation system which has been successfully applied in many applications.However,it suffers from serious problems of cold start and data sparsity.In addition,these methods can not indicate their confidence in recommendation.In this paper,we improved the random walk model combining trust-based and item-based collaborative filtering method for recommendation.The trust factor is introduced as an important factor of guiding recommendations.The random walk model considers not only the ratings of target item,but also those of the similar items.The probability of using the rating of a the similar item instead of a ra-ting for the target item increases with increasing length of walk.Our framework contains both trust-based and item-based collaborative filtering recommendations as special cases.The empirical analysis on the Epinions dataset demonstrates that our method can provide better recommendation result in terms of evaluation metrics than other algorithms.

Key words: Collaborative filtering,Recommender system,Random walk,Trust network