Abstract: With the advent of service computing,many different electronic services have emerged.Users often have to find what they need from a large number of services,which is a formidable task.Hence,it is necessary to put forward an efficient recommendation algorithm.The traditional cooperative recommendation system has some problems,such as cold start,sparsity of data and poor real-time performance,which lead to poor recommendation results under the circumstances with less scoring data.In order to get a better recommendation result,this paper introduced trust transfer in social networks and utilized it to establish a trust transfer model to obtain trust among users.On the other hand,based on the score data,the similarity between users in the system is calculated.On the basis of similarity between users’ trust and preference,according to the characteristics of social networks,users’ trust and preference are dynamically combined to obtain comprehensive recommendation weights.The comprehensive recommendation weights can replace the traditional similarity measurement standards for user-based collaborative filtering recommendation.This method was verified through the Epinions data set and can further improve the recommendation effect and.

Key words: Cold start, Collaborative filtering, Recommendation system, Trust network