Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
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 link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
An accelerated gradient method for trace norm minimization
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Efficient Euclidean projections in linear time
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Trust based recommender system for the semantic web
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exact Matrix Completion via Convex Optimization
Foundations of Computational Mathematics
Predicting positive and negative links in online social networks
Proceedings of the 19th international conference on World wide web
The power of convex relaxation: near-optimal matrix completion
IEEE Transactions on Information Theory
On the quality of inferring interests from social neighbors
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Limitations of matrix completion via trace norm minimization
ACM SIGKDD Explorations Newsletter
Multi-Class L2,1-Norm Support Vector Machine
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Unsupervised and semi-supervised learning via l1-norm graph
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Trust prediction via aggregating heterogeneous social networks
Proceedings of the 21st ACM international conference on Information and knowledge management
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Trust prediction, which explores the unobserved relationships between online community users, is an emerging and important research topic in social network analysis and many web applications. Similar to other social-based recommender systems, trust relationships between users can be also modeled in the form of matrices. Recent study shows users generally establish friendship due to a few latent factors, it is therefore reasonable to assume the trust matrices are of low-rank. As a result, many recommendation system strategies can be applied here. In particular, trace norm minimization, which uses matrix's trace norm to approximate its rank, is especially appealing. However, recent articles cast doubts on the validity of trace norm approximation. In this paper, instead of using trace norm minimization, we propose a new robust rank-k matrix completion method, which explicitly seeks a matrix with exact rank. Moreover, our method is robust to noise or corrupted observations. We optimize the new objective function in an alternative manner, based on a combination of ancillary variables and Augmented Lagrangian Multiplier (ALM) Method. We perform the experiments on three real-world data sets and all empirical results demonstrate the effectiveness of our method.