From Neighbors to Global Neighbors in Collaborative Filtering: an Evolutionary Optimization Approach

Abstract : The accuracy of recommendations of collaborative filtering bas\-ed 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.
Type de document :
Communication dans un congrès
GECCO - Genetic and Evolutionary Computation Conference - 2012, Jul 2012, Philadelphia, United States. ACM, pp.345-352, 2012, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO'12. 〈10.1145/2330163.2330214〉
Liste complète des métadonnées

https://hal.inria.fr/hal-00778495
Contributeur : Armelle Brun <>
Soumis le : dimanche 20 janvier 2013 - 17:15:32
Dernière modification le : mardi 24 avril 2018 - 13:34:07

Identifiants

Collections

Citation

Amine Boumaza, Armelle Brun. From Neighbors to Global Neighbors in Collaborative Filtering: an Evolutionary Optimization Approach. GECCO - Genetic and Evolutionary Computation Conference - 2012, Jul 2012, Philadelphia, United States. ACM, pp.345-352, 2012, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference - GECCO'12. 〈10.1145/2330163.2330214〉. 〈hal-00778495〉

Partager

Métriques

Consultations de la notice

149