Stochastic Search for Global Neighbors Selection in Collaborative Filtering

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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Communication dans un congrès
SAC - 27th Annual ACM Symposium on Applied Computing - 2012, Mar 2012, Riva del Garda, Italy. ACM, pp.232-237, 2012, Proceedings of the 27th Annual ACM Symposium on Applied Computing SAC'12. 〈10.1145/2245276.2245322〉
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Contributeur : Armelle Brun <>
Soumis le : dimanche 20 janvier 2013 - 17:19:38
Dernière modification le : mardi 24 avril 2018 - 13:34:01

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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. ACM, pp.232-237, 2012, Proceedings of the 27th Annual ACM Symposium on Applied Computing SAC'12. 〈10.1145/2245276.2245322〉. 〈hal-00778497〉

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