Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
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
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
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
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
A collaborative filtering algorithm and evaluation metric that accurately model the user experience
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
Collaborative Filtering Process in a Whole New Light
IDEAS '06 Proceedings of the 10th International Database Engineering and Applications Symposium
Latent class models for collaborative filtering
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
WebKDD 2006: web mining and web usage analysis post-workshop report
ACM SIGKDD Explorations Newsletter
A Multi-Objective Multipopulation Approach for Biclustering
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Improving a multi-objective multipopulation artificial immune network for biclustering
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Query expansion using an immune-inspired biclustering algorithm
Natural Computing: an international journal
Interest-based real-time content recommendation in online social communities
Knowledge-Based Systems
Feature enriched nonparametric bayesian co-clustering
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Predicting missing values with biclustering: A coherence-based approach
Pattern Recognition
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Collaborative Filtering (CF) Systems have been studied extensively for more than a decade to confront the "information overload" problem. Nearest-neighbor CF is based either on common user or item similarities, to form the user's neighborhood. The effectiveness of the aforementioned approaches would be augmented, if we could combine them. In this paper, we use biclustering to disclose this duality between users and items, by grouping them in both dimensions simultaneously. We propose a novel nearest-biclusters algorithm, which uses a new similarity measure that achieves partial matching of users' preferences. We apply nearest-biclusters in combination with a biclustering algorithm - Bimax - for constant values. Extensively performance evaluations on two real data sets is provided, which show that the proposed method improves the performance of the CF process substantially. We attain more than 30% and 10% improvement in terms of precision and recall, respectively.