Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Eigentrust algorithm for reputation management in P2P networks
WWW '03 Proceedings of the 12th international conference on World Wide Web
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Convex Optimization
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the quality of inferring interests from social neighbors
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Exact matrix completion via convex optimization
Communications of the ACM
Social trust prediction using rank-k matrix recovery
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Social trust prediction using heterogeneous networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Along with the increasing popularity of social web sites, users rely more on the trustworthiness information for many online activities among users. However, such social network data often suffers from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches explore the topology of trust graph. Previous research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behavior and tastes. Such ancillary information, is often accessible and therefore could potentially help the trust prediction. In this paper, we address the link prediction problem by aggregating heterogeneous social networks and propose a novel joint manifold factorization (JMF) method. Our new joint learning model explores the user group level similarity between correlated graphs and simultaneously learns the individual graph structure, therefore the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph, but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the objective function, we break down the proposed objective function into several manageable sub-problems, then further establish the theoretical convergence with the aid of auxiliary function. Extensive experiments were conducted on real world data sets and all empirical results demonstrated the effectiveness of our method.