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
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Comparing Recommendation Strategies in a Commercial Context
IEEE Intelligent Systems
Effective missing data prediction for collaborative filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
A recursive prediction algorithm for collaborative filtering recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
A Comparison of Collaborative-Filtering Recommendation Algorithms for E-commerce
IEEE Intelligent Systems
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Boosting collaborative filtering based on statistical prediction errors
Proceedings of the 2008 ACM conference on Recommender systems
List-wise learning to rank with matrix factorization for collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Mining mood-specific movie similarity with matrix factorization for context-aware recommendation
Proceedings of the Workshop on Context-Aware Movie Recommendation
Informative household recommendation with feature-based matrix factorization
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Improving the performance of recommender system by exploiting the categories of products
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Collaborative personalized tweet recommendation
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Local implicit feedback mining for music recommendation
Proceedings of the sixth ACM conference on Recommender systems
The efficient imputation method for neighborhood-based collaborative filtering
Proceedings of the 21st ACM international conference on Information and knowledge management
Mining contextual movie similarity with matrix factorization for context-aware recommendation
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
Unifying rating-oriented and ranking-oriented collaborative filtering for improved recommendation
Information Sciences: an International Journal
A novel Bayesian similarity measure for recommender systems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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An approach to user-based collaborative filtering is proposed that refines prediction of item ratings that is based on global user similarity by incorporating information derived from a more detailed user comparison made on the basis of Rated Item Pools (RIPs). The preference spectrum defined by items that a user has rated, and ranging from best-liked to most disliked items, is divided into item sets, or RIPs, which supply the basis for a fine-grained calculation of similarity between users. The RIP-based approach makes it possible for the model to take advantage of user tastes that are matched at one end of the spectrum, e.g., two users agree on favorites, without requiring complete correspondence of item ratings between user profiles. The approach improves rating prediction, as compared to a baseline that uses the global user similarity alone. It does not unduly inflate computational complexity or rely on external resources, common shortcomings of competing rating prediction methods. Cases in which the nearest neighbors are relatively dissimilar, known to be challenging for user-based collaborative filtering, demonstrate particularly substantial improvement. Performance is shown to be stable across the choice of neighborhood size, number of pools and relative pool size.