Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Communications of the ACM
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd 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
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Proceedings of the 10th international conference on Intelligent user interfaces
IEEE Transactions on Knowledge and Data Engineering
Using SVD and demographic data for the enhancement of generalized Collaborative Filtering
Information Sciences: an International Journal
A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem
Information Sciences: an International Journal
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Unified relevance models for rating prediction in collaborative filtering
ACM Transactions on Information Systems (TOIS)
Comparing State-of-the-Art Collaborative Filtering Systems
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Re-considering neighborhood-based collaborative filtering parameters in the context of new data
Proceedings of the 17th ACM conference on Information and knowledge management
Collaborative filtering adapted to recommender systems of e-learning
Knowledge-Based Systems
Managing uncertainty in group recommending processes
User Modeling and User-Adapted Interaction
Collaborative prediction and ranking with non-random missing data
Proceedings of the third ACM conference on Recommender systems
Measuring predictive capability in collaborative filtering
Proceedings of the third ACM conference on Recommender systems
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
A new collaborative filtering metric that improves the behavior of recommender systems
Knowledge-Based Systems
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Precision-oriented evaluation of recommender systems: an algorithmic comparison
Proceedings of the fifth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A new criteria for selecting neighborhood in memory-based recommender systems
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
Top-N news recommendations in digital newspapers
Knowledge-Based Systems
Goal-driven collaborative filtering – a directional error based approach
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
An effective recommendation method for cold start new users using trust and distrust networks
Information Sciences: an International Journal
Information Processing and Management: an International Journal
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This paper presents a new approach for memory-based collaborative filtering algorithms. In general, user-based rating prediction is a process in which each neighbor suggests a rating for the target item and the suggestions are combined by weighting the contribution of each neighbor. We present a new alternative that is independent of user rating scales and is based on what we call predictive probabilities. We explore how these probabilities can be used to select nearest neighbors for recommendations and integrate different types of dependence in the ratings. The neighborhood selection criterion depends on the capability of a user to predict past ratings. Our hypothesis is that if a user was good when predicting past ratings for an active user, then his predictions will also be helpful in recommending ratings for the same user in the future.