GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Learning with Genetic Algorithms: An Overview
Machine Learning
Learning User Similarity and Rating Style for Collaborative Recommendation
Information Retrieval
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Knowledge and Data Engineering
A new framework to combine descriptors for content-based image retrieval
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy-genetic approach to recommender systems based on a novel hybrid user model
Expert Systems with Applications: An International Journal
Designing Specific Weighted Similarity Measures to Improve Collaborative Filtering Systems
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Learning Bidirectional Similarity for Collaborative Filtering
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Fuzzy computational models for trust and reputation systems
Electronic Commerce Research and Applications
Using evolution programs to learn local similarity measures
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
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Collaborative Filtering techniques offer recommendations to users by leveraging on the preferences of like-minded users. They thus rely highly on similarity measures to determine proximity between users. However, most of the previously proposed similarity measures are heuristics based and are not guaranteed to work well under all data environments. We propose a method employing Genetic algorithm to learn user similarity based on comparison of individual hybrid user features. The user similarity is determined for each feature by learning a feature similarity function. The rating for each item is then predicted as an aggregate of estimates garnered from predictors based on each attribute. Our method differs from previous attempts at learning similarity, as the features considered for comparison take into account not only user preferences but also the item contents and user demographic data. The proposed method is shown to outperform existing filtering methods based on user-defined similarity measures.