Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
Guided Local Search with Shifting Bottleneck for Job Shop Scheduling
Management Science
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
Evaluating collaborative filtering recommender systems
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
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A Client/Server User-Based Collaborative Filtering Algorithm: Model and Implementation
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
An effective threshold-based neighbor selection in collaborative filtering
ECIR'07 Proceedings of the 29th European conference on IR research
Improving the scalability of recommender systems by clustering using genetic algorithms
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in 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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Neighborhood based collaborative filtering is a popular approach in recommendation systems. In this paper we propose to apply evolutionary computation to reduce the size of the model used for the recommendation. We formulate the problem of constructing the set of neighbors as an optimization problem that we tackle by stochastic local search. The results we present show that our approach produces a set of global neighbors made up of less than 16% of the entire set of users, thus decreases the size of the model by 84%. Furthermore, this reduction leads to a slight increase of the accuracy of a state of the art clustering based approach, without impacting the coverage.