GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Fab: content-based, collaborative recommendation
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Co-clustering documents and words using bipartite spectral graph partitioning
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Towards a robust fuzzy clustering
Fuzzy Sets and Systems - Data analysis
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Collaborative Filtering Using a Regression-Based Approach
Knowledge and Information Systems
IEEE Transactions on Knowledge and Data Engineering
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Introduction to Information Retrieval
Introduction to Information Retrieval
Probabilistic dyadic data analysis with local and global consistency
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Fast nonparametric matrix factorization for large-scale collaborative filtering
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collaborative filtering by personality diagnosis: a hybrid memory- and model-based approach
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Locally Discriminative Coclustering
IEEE Transactions on Knowledge and Data Engineering
A general collaborative filtering framework based on matrix bordered block diagonal forms
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Improve collaborative filtering through bordered block diagonal form matrices
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
SoCo: a social network aided context-aware recommender system
Proceedings of the 22nd international conference on World Wide Web
Whom to mention: expand the diffusion of tweets by @ recommendation on micro-blogging systems
Proceedings of the 22nd international conference on World Wide Web
TopRec: domain-specific recommendation through community topic mining in social network
Proceedings of the 22nd international conference on World Wide Web
Localized matrix factorization for recommendation based on matrix block diagonal forms
Proceedings of the 22nd international conference on World Wide Web
Detecting profilable and overlapping communities with user-generated multimedia contents in LBSNs
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Community-based user recommendation in uni-directional social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Clustering-based factorized collaborative filtering
Proceedings of the 7th ACM conference on Recommender systems
iNewsBox: modeling and exploiting implicit feedback for building personalized news radio
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Agent-based architecture for context-aware and personalized event recommendation
Expert Systems with Applications: An International Journal
Heterogeneous graph-based intent learning with queries, web pages and Wikipedia concepts
Proceedings of the 7th ACM international conference on Web search and data mining
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Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach.