earticle

영어

A new algorithm FTRA has been proposed, which infuses users’ trust network and rating data. The sparse problem of rating data will significantly reduce the accuracy of collaborative filtering recommendation. In addition to the users’ ratings data on the Internet, other data sources which can be used in the process of recommend, and one of the more common is trust network data which describes the mutual relationship between users. To solve this problem, this paper will the data of trust network as an important supplement on the rating data, and bases on graph theory concepts or methods, the similarity method in the paper, and the Katz method which is used to calculate the similarity of link, proposes the FTRA algorithm which organic infuses this two data, and then better to solve the sparse problem of the rating data faced by collaborative filtering. The experimental results on the Epinions dataset show that the FTRA algorithm is superior to or significantly better than the comparison algorithms, which include the algorithms that only based on the rating data or the trust relationship, and the other algorithms infusing the two data sources.