Diffusing multi-aspects of local and global social trust for personalizing trust enhanced recommender system

Recommender systems are an effective solution to the information overload problem, especially in the World Wide Web where we gather vast information from anonymous people around the world. Trust enhanced recommender system to be promising to overcome the cold-start and sparsity challenges of traditional recommender system as well as improving the accuracy of the recommendations. This arise a research focus about blending of the trust information and trust level prediction to the recommendation framework. From the past decade, numerous researches were done to adapt online social network trust (simply social trust) for many web applications, including e-commerce, P2P networks, multi-agent systems, recommendation systems, and service-oriented computing. Usually, online social trust prediction can be based on two mechanisms to acquire trust value: evaluating trustee on basis of truster/truster's neighbor trust experience information (local trust), otherwise evaluating trustee on the basis of the whole social network trust experience information as reputation (global trust). Here, we leverage social science theories to develop the trust models that enable the study of online social trust evolution. In this paper, we propose a matrix factorization based trust enhanced recommendation system which properly incorporates both local trust and global trust with diffusion of the social trust multi-aspects to improve the quality of recommendations for mitigating the data sparsity and the cold-start issues. Through experiments on the Epinions data set, we show that our model outperforms its standard trust enhanced counterparts with respect to accuracy on recommender systems.