Collaborative Filtering Recommendation Algorithm Incorporating User Implicit Trust
LANG Ya-kun,WANG Guo-zhong
(Shanghai University of Engineering Science,Department of Electronic and Electrical Engineering,Shanghai 201620,China)(Key Laboratory of Artificial Intelligence Application State Administration of Radio and Television,Shanghai 201620,China)
Abstract：In order to solve the problems of sparse data,poor correlation and cold start in traditional recommendation systems,some scholars proposed to introduce the trust relationship in social networks into the recommendation system to form the social recommendation.This approach improvs the accuracy of the recommendation to some extent,but the explicit trust information is difficult to obtain and the existing trust information is very sparse.Aiming at the shortcomings of the algorithm of adding user trust information,we propose a collaborative filtering recommendation model named FITrustSVD that incorporates user′s implicit trust.This model incorporates the implicit trust of users on the basis of the TrustSVD algorithm,it defines the implicit trust to correct the trust information between users and limits the range of trusted users.We improve the calculation formula of the similarity algorithm and add user's trust bias into the trust prediction formula.Experiments show that the improved model has higher recommendation accuracy under the conditions of sparse data and cold start.
2021, Vol. 42 
