Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Knowledge and Information Systems
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Cross-Domain Mediation in Collaborative Filtering
UM '07 Proceedings of the 11th international conference on User Modeling
Collaborative filtering recommender systems
The adaptive web
Predicting Neighbor Goodness in Collaborative Filtering
FQAS '09 Proceedings of the 8th International Conference on Flexible Query Answering Systems
Predicting performance in recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Extracting and exploiting topics of interests from social tagging systems
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Improving collaborative filtering in social tagging systems
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A performance prediction approach to enhance collaborative filtering performance
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
An entropy-based neighbor selection approach for collaborative filtering
Knowledge-Based Systems
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User-to-user similarity is a fundamental component of Collaborative Filtering (CF) recommender systems. In user-to-user similarity the ratings assigned by two users to a set of items are pairwise compared and averaged (correlation). In this paper we make user-to-user similarity adaptive, i.e., we dynamically change the computation depending on the profiles of the compared users and the target item whose rating prediction is sought. We propose to base the similarity between two users only on the subset of co-rated items which best describes the taste of the users with respect to the target item. These are the items which have the highest correlation with the target item. We have evaluated the proposed method using a range of error measures and showed that the proposed locally adaptive neighbor selection, via item selection, can significantly improve the recommendation accuracy compared to standard CF.