Issue No. 07 - July (2014 vol. 26)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2013.147
Yuan Yao , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Hanghang Tong , School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Phoenix, AZ, USA
Xifeng Yan , , University of California at Santa Barbara, Santa Barbara, CA, USA
Feng Xu , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Jian Lu , State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
Inferring the pair-wise trust relationship is a core building block for many real applications. State-of-the-art approaches for such trust inference mainly employ the transitivity property of trust by propagating trust along connected users, but largely ignore other important properties such as trust bias, multi-aspect, etc. In this paper, we propose a new trust inference model to integrate all these important properties. To apply the model to both binary and continuous inference scenarios, we further propose a family of effective and efficient algorithms. Extensive experimental evaluations on real data sets show that our method achieves significant improvement over several existing benchmark approaches, for both quantifying numerical trustworthiness scores and predicting binary trust/distrust signs. In addition, it enjoys linear scalability in both time and space.
Y. Yao, H. Tong, X. Yan, F. Xu and J. Lu, "Multi-Aspect + Transitivity + Bias: An Integral Trust Inference Model," in IEEE Transactions on Knowledge & Data Engineering, vol. 26, no. 7, pp. 1706-1719, 2014.