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
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
A graph-based recommender system for digital library
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Learning Collaborative Information Filters
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
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)
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 Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
A Generalized Maximum Entropy Approach to Bregman Co-clustering and Matrix Approximation
The Journal of Machine Learning Research
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Collaborative filtering based on transitive correlations between items
ECIR'07 Proceedings of the 29th European conference on IR research
Which photo groups should I choose? A comparative study of recommendation algorithms in Flickr
Journal of Information Science
A probabilistic approach to semantic collaborative filtering using world knowledge
Journal of Information Science
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
A levelwise spectral co-clustering algorithm for collaborative filtering
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
Hierarchical graph maps for visualization of collaborative recommender systems
Journal of Information Science
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Collaborative filtering is a widely used approach in recommendation systems which predict user preferences by learning from user-item ratings. To extract either user relationship or item dependencies, there exist several well known approaches; among them clustering is of great importance. Traditional clustering methods in collaborative filtering usually suffer from two fundamental problems: sparsity and scalability. Sparsity refers to a situation where most users rate only a small number of items, while scalability denotes a huge number of both users and items. Inspired by these problems, this paper presents a novel stepwise paradigm, SPCF, which in the first step clusters users and items separately using their latent similarity. Once the primary clusters of the first level are formed, the second level simultaneously clusters the user and item clusters by means of co-clustering. The advantages of SPCF are threefold; first, it is able to alleviate the well known sparsity problem which intrinsically exists in collaborative filtering; second, the proposed method offers an elegant solution to the scalability problem based on dimensionality reduction which in turn leads to better performance of the model; third, experimental results on two versions of a Movielens dataset for prediction have demonstrated that the proposed method can reveal major interests of users or items in promising manner.