Reducing offline evaluation bias of collaborative filtering algorithms

Abstract : Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.
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Communication dans un congrès
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015)
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Contributeur : Fabrice Rossi <>
Soumis le : vendredi 12 juin 2015 - 17:09:53
Dernière modification le : samedi 13 juin 2015 - 01:05:40

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  • HAL Id : hal-01163390, version 1
  • ARXIV : 1506.04135

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Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi. Reducing offline evaluation bias of collaborative filtering algorithms. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), Apr 2015, Bruges, Belgium. pp.137-142, 2015, Proceedings of the 23-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015). <hal-01163390>

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