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Évaluation hors-ligne d'un modèle prédictif : application aux algorithmes de recommandation et à la minimisation de l'erreur relative moyenne

Abstract : The offline evaluation permits to estimate the quality of a predictive model using historical data before deploying the model in production. To be efficient, the data used to compute the offline evaluation must be representative of real data. In this thesis we describe the case when the historical data is biased. Through experiments done at Viadeo (french professional social network) we suggest a new offline evaluation procedure to estimate the quality of a recommendation algorithm when the data is biased. Then we introduce the concept of Explanatory Shift, which is a particular case of bias, and we suggest a new approach to build an efficient model under Explanatory Shift. In the second part of this thesis we discuss the importance of the loss function used to select a model using the empirical risk minimization method (ERM), and we study in detail the particular case of the Mean Absolute Percentage Error (MAPE). First we analyze necessary conditions to ensure that the risk is well defined. Then we show that the model obtained by ERM is consistant under some assumptions.
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https://tel.archives-ouvertes.fr/tel-01395290
Contributor : Arnaud de Myttenaere Connect in order to contact the contributor
Submitted on : Wednesday, November 16, 2016 - 12:01:39 PM
Last modification on : Friday, May 6, 2022 - 4:50:07 PM
Long-term archiving on: : Thursday, March 16, 2017 - 11:36:33 AM

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  • HAL Id : tel-01395290, version 1

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Arnaud de Myttenaere. Évaluation hors-ligne d'un modèle prédictif : application aux algorithmes de recommandation et à la minimisation de l'erreur relative moyenne. Machine Learning [stat.ML]. Université paris 1 Panthéon-La Sorbonne, 2016. Français. ⟨tel-01395290⟩

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