Concepts and effectiveness of the cover-coefficient-based clustering methodology for text databases
ACM Transactions on Database Systems (TODS)
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Inverted file search algorithms for collaborative filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Efficiency and effectiveness of query processing in cluster-based retrieval
Information Systems
IEEE Transactions on Knowledge and Data Engineering
Scalable collaborative filtering using cluster-based smoothing
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic music recommender considering user opinions and audio features
Information Processing and Management: an International Journal - Special issue: AIRS2005: Information retrieval research in Asia
Incremental cluster-based retrieval using compressed cluster-skipping inverted files
ACM Transactions on Information Systems (TOIS)
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
Using Context to Improve Predictive Modeling of Customers in Personalization Applications
IEEE Transactions on Knowledge and Data Engineering
Improving Accuracy of Recommender System by Item Clustering
IEICE - Transactions on Information and Systems
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Novel Item Recommendation by User Profile Partitioning
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
International Journal of Approximate Reasoning
A hybrid recommendation method with reduced data for large-scale application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Using external aggregate ratings for improving individual recommendations
ACM Transactions on the Web (TWEB)
Text retrieval methods for item ranking in collaborative filtering
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Algorithms for within-cluster searches using inverted files
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
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In-memory nearest neighbor computation is a typical collaborative filtering approach for high recommendation accuracy. However, this approach is not scalable given the huge number of customers and items in typical commercial applications. Cluster-based collaborative filtering techniques can be a remedy for the efficiency problem, but they usually provide relatively lower accuracy figures, since they may become over-generalized and produce less-personalized recommendations. Our research explores an individualistic strategy which initially clusters the users and then exploits the members within clusters, but not just the cluster representatives, during the recommendation generation stage. We provide an efficient implementation of this strategy by adapting a specifically tailored cluster-skipping inverted index structure. Experimental results reveal that the individualistic strategy with the cluster-skipping index is a good compromise that yields high accuracy and reasonable scalability figures.