GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
A generalized maximum entropy approach to bregman co-clustering and matrix approximation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
SPCF: a stepwise partitioning for collaborative filtering to alleviate sparsity problems
Journal of Information Science
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Collaborative Filtering (CF) is a widely used approach in recommendation systems that predicts user preferences by learning from user-item ratings. To extract either user relationship or item dependencies, there exists several well known approaches; among them clustering is of great importance. Inspired by this understanding, this paper presents a novel levelwise paradigm whose first level clusters users and items separately using their pair wise similarity. Once the primary clusters of first level formed, the second level, does the simultaneously clustering user and item clusters by the means of Co-clustering. The main advantages of our approach include: First, it is able to alleviate the well known notion of sparsity problem intrinsically exists in CF. Second, the proposed method offers an elegant dimensionality reduction what in turn leads to better performance. Third experimental results on Movielens dataset have demonstrated that the proposed method can effectively reveal the subset aggregates of users and items which are closely related.