Study of a bias in the offline evaluation of a recommendation algorithm

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 describes this bias and discuss the relevance of a weighted offline evaluation to reduce this bias for different classes of recommendation algorithms.
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Communication dans un congrès
Petra Perner. 11th Industrial Conference on Data Mining, ICDM 2015, Jul 2015, Hamburg, Germany. Ibai Publishing, pp.57-70, 2015, Advances in Data Mining
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Contributeur : Fabrice Rossi <>
Soumis le : jeudi 29 octobre 2015 - 19:17:01
Dernière modification le : jeudi 5 novembre 2015 - 01:06:14
Document(s) archivé(s) le : vendredi 28 avril 2017 - 07:04:55

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Distributed under a Creative Commons Paternité - Partage selon les Conditions Initiales 4.0 International License

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  • HAL Id : hal-01222395, version 1
  • ARXIV : 1511.01280

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Arnaud De Myttenaere, Boris Golden, Bénédicte Le Grand, Fabrice Rossi. Study of a bias in the offline evaluation of a recommendation algorithm. Petra Perner. 11th Industrial Conference on Data Mining, ICDM 2015, Jul 2015, Hamburg, Germany. Ibai Publishing, pp.57-70, 2015, Advances in Data Mining. <hal-01222395>

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