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Stochastic Search for Global Neighbors Selection in Collaborative Filtering

Amine Boumaza 1 Armelle Brun 2 
2 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : Neighborhood based collaborative filtering is a popular ap- proach 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 prob- lem 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.
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Submitted on : Sunday, January 20, 2013 - 5:19:38 PM
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Amine Boumaza, Armelle Brun. Stochastic Search for Global Neighbors Selection in Collaborative Filtering. SAC - 27th Annual ACM Symposium on Applied Computing - 2012, Mar 2012, Riva del Garda, Italy. pp.232-237, ⟨10.1145/2245276.2245322⟩. ⟨hal-00778497⟩



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