An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
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
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Convex Optimization
Fast sparse matrix multiplication
ACM Transactions on Algorithms (TALG)
ACM SIGKDD Explorations Newsletter
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Combining content and link for classification using matrix factorization
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A tutorial on spectral clustering
Statistics and Computing
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Feedback effects between similarity and social influence in online communities
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Large-scale collaborative prediction using a nonparametric random effects model
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-relational learning with Gaussian processes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Convex and Semi-Nonnegative Matrix Factorizations
IEEE Transactions on Pattern Analysis and Machine Intelligence
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
On the quality of inferring interests from social neighbors
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Semi-Supervised Learning
Exact matrix completion via convex optimization
Communications of the ACM
Trust prediction via aggregating heterogeneous social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
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Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer 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 are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) 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 proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.