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
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)
IEEE Transactions on Knowledge and Data Engineering
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 Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Model-based collaborative filtering as a defense against profile injection attacks
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
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
A performance prediction approach to enhance collaborative filtering performance
ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
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The accuracy of recommendations of collaborative filtering based recommender systems mainly depends on which users (the neighbors) are exploited to estimate a user's ratings. We propose a new approach of neighbor selection, which adopts a global point of view. This approach defines a unique set of possible neighbors, shared by all users, referred to as Global Neighbors (GN). We view the problem of defining GN as a combinatorial optimization problem and propose to use an evolutionary algorithm to tackle this search. Our aim is to find a relatively small GN as the size of the resulting model, as well as the complexity of the computation of recommendations highly depend on the size of GN. We present experiments and results on a standard benchmark data-set from the recommender system community that support our choice of the evolutionary approach and show that it leads to a high accuracy of recommendations and a high coverage, while dramatically reducing the size of the model (by 84%). We also show that the evolutionary approach produces results able to generate accurate recommendations to unseen users, while easily allowing the insertion of new users in the system with little overhead.