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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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https://hal.archives-ouvertes.fr/hal-01163390
Contributor : Fabrice Rossi <>
Submitted on : Friday, June 12, 2015 - 5:09:53 PM
Last modification on : Sunday, January 19, 2020 - 6:38:32 PM
Long-term archiving on: : Tuesday, April 25, 2017 - 7:38:28 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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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. ⟨hal-01163390⟩

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