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

2 KIWI - Knowledge Information and Web Intelligence
LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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.
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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〉
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https://hal.inria.fr/hal-00778495
Contributeur : Armelle Brun <>
Soumis le : dimanche 20 janvier 2013 - 17:15:32
Dernière modification le : mardi 18 décembre 2018 - 16:40:21

### 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〉

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