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
CiteSeer: an automatic citation indexing system
Proceedings of the third ACM conference on Digital libraries
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Document clustering using word clusters via the information bottleneck method
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
SWAMI (poster session): a framework for collaborative filtering algorithm development and evaluation
SIGIR '00 Proceedings of the 23rd 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
Bipartite graph partitioning and data clustering
Proceedings of the tenth international conference on Information and knowledge management
Collaborative filtering with privacy via factor analysis
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Eigentaste: A Constant Time Collaborative Filtering Algorithm
Information Retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory and Model-Based Approach
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Co-clustering by block value decomposition
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fast maximum margin matrix factorization for collaborative prediction
ICML '05 Proceedings of the 22nd international conference on Machine learning
A Scalable Collaborative Filtering Framework Based on Co-Clustering
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Unifying user-based and item-based collaborative filtering approaches by similarity fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Orthogonal nonnegative matrix t-factorizations for clustering
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning bidirectional asymmetric similarity for collaborative filtering via matrix factorization
Data Mining and Knowledge Discovery
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Co-clustering with augmented data matrix
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
An improved privacy-preserving DWT-based collaborative filtering scheme
Expert Systems with Applications: An International Journal
Collaborative Filtering with a User-Item Matrix Reduction Technique
International Journal of Electronic Commerce
A collaborative filtering similarity measure based on singularities
Information Processing and Management: an International Journal
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Enhancement of information seeking using an information needs radar model
Information Processing and Management: an International Journal
Low-Rank Matrix Approximation with Weights or Missing Data Is NP-Hard
SIAM Journal on Matrix Analysis and Applications
Evaluating collaborative filtering recommendations inside large learning object repositories
Information Processing and Management: an International Journal
A comparison of clustering-based privacy-preserving collaborative filtering schemes
Applied Soft Computing
A scalable privacy-preserving recommendation scheme via bisecting k-means clustering
Information Processing and Management: an International Journal
Co-clustering with augmented matrix
Applied Intelligence
Collaborative filtering based on rating psychology
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Collaborative filtering using multidimensional psychometrics model
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Expert Systems with Applications: An International Journal
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Collaborative filtering aims at predicting a test user's ratings for new items by integrating other like-minded users' rating information. The key assumption is that users sharing the same ratings on past items tend to agree on new items. Traditional collaborative filtering methods can mainly be divided into two classes: memory-based and model-based. The memory-based approaches generally suffer from two fundamental problems: sparsity and scalability, and the model-based approaches usually cost too much on establishing a model and have many parameters to be tuned. In this paper, we propose a novel framework for collaborative filtering by applying orthogonal nonnegative matrix tri-factorization (ONMTF), which (1) alleviates the sparsity problem via matrix factorization; (2) solves the scalability problem by simultaneously clustering rows and columns of the user-item matrix. Experiments on the benchmark data set show that our algorithm is indeed more tolerant against both sparsity and scalability, and achieves good performance in the mean time.