Finding good approximate vertex and edge partitions is NP-hard
Information Processing Letters
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs
SIAM Journal on Scientific Computing
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Permuting Sparse Rectangular Matrices into Block-Diagonal Form
SIAM Journal on Scientific Computing
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Exploring Local Community Structures in Large Networks
WI '06 Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Recommender System for Music CDs Using a Graph Partitioning Method
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
A NMF-Based Privacy-Preserving Recommendation Algorithm
ICISE '09 Proceedings of the 2009 First IEEE International Conference on Information Science and Engineering
Content-based recommendation systems
The adaptive web
Distributed nonnegative matrix factorization for web-scale dyadic data analysis on mapreduce
Proceedings of the 19th international conference on World wide web
Community discovery using nonnegative matrix factorization
Data Mining and Knowledge Discovery
Genetic approaches for graph partitioning: a survey
Proceedings of the 13th annual conference on Genetic and evolutionary computation
CLR: a collaborative location recommendation framework based on co-clustering
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Regularized latent semantic indexing
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Large-scale matrix factorization with distributed stochastic gradient descent
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Engineering multilevel graph partitioning algorithms
ESA'11 Proceedings of the 19th European conference on Algorithms
An exploration of improving collaborative recommender systems via user-item subgroups
Proceedings of the 21st international conference on World Wide Web
Community detection in incomplete information networks
Proceedings of the 21st international conference on World Wide Web
New objective functions for social collaborative filtering
Proceedings of the 21st international conference on World Wide Web
Dense Subgraph Extraction with Application to Community Detection
IEEE Transactions on Knowledge and Data Engineering
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Collaborative Filtering-based recommendation algorithms have achieved widespread success on the Web, but little work has been performed to investigate appropriate user-item relationship structures of rating matrices. This paper presents a novel and general collaborative filtering framework based on (Approximate) Bordered Block Diagonal Form structure of user-item rating matrices. We show formally that matrices in (A)BBDF structures correspond to community detection on the corresponding bipartite graphs, and they reveal relationships among users and items intuitionally in recommendation tasks. By this framework, general and special interests of a user are distinguished, which helps to improve prediction accuracy in collaborative filtering tasks. Experimental results on four real-world datasets, including the Yahoo! Music dataset, which is currently the largest, show that the proposed framework helps many traditional collaborative filtering algorithms, such as User-based, Item-based, SVD and NMF approaches, to make more accurate rating predictions. Moreover, by leveraging smaller and denser submatrices to make predictions, this framework contributes to the scalability of recommender systems.