iSim: An efficient integrated similarity based collaborative filtering approach for trust prediction in service-oriented social networks

Abstract

Service-oriented social networks gain increasing popularity among a huge user base in recent years. In social networks, trust prediction is significant for recommendations of high-quality service providers as well as in many other applications. In the literature, trust prediction problem can be solved by several strategies, such as matrix factorization, trust propagation, and K-NN search, etc. However, most of the existing works have not considered the possible complementarity among these mainstream strategies to optimize their effectiveness and efficiency. In this paper, we propose a novel trust prediction approach named iSim: an integrated similarity based collaborative filtering approach leveraging on user similarity, which integrates three kinds of factors to measure user similarity, including vector space similarity, matrix factorization, and propagated trust. This paper is the first work in the literature employing matrix factorization and propagated trust in the study of similarity. Additionally, we use several methods like adding inverted index to reduce the time complexity of iSim, and provide its theoretical time bound. Finally, the extensive experiments with real-world dataset show that iSim achieves great improvement for both efficiency and effectiveness over the state-of-the-art approaches.