Uplift modeling for recommendation system

Atef Shaar (), Talel Abdessalem () and Olivier Segard ()
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Atef Shaar: LTCI - Laboratoire Traitement et Communication de l'Information - Télécom ParisTech - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique, INFRES - Département Informatique et Réseaux - Télécom ParisTech
Talel Abdessalem: LTCI - Laboratoire Traitement et Communication de l'Information - Télécom ParisTech - IMT - Institut Mines-Télécom [Paris] - CNRS - Centre National de la Recherche Scientifique, INFRES - Département Informatique et Réseaux - Télécom ParisTech
Olivier Segard: IMT-BS - MMS - Département Management, Marketing et Stratégie - TEM - Télécom Ecole de Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - TEM - Télécom Ecole de Management

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Abstract: Uplift Modeling is a branch of machine learning which aims at predicting the causal effect of an action on a given individual. It aims to predict not the class itself, but the difference between the class variable behaviors in two groups. By using uplift modeling for recommender system, we can differentiate between the effects of two treatment and specify the best treatment based on its impact on customer behavior. We applied uplift modeling algorithms on marketing campaign dataset, we measured the real impact of the each treatment and optimized the recommender system by sub-targeting and personalizing.

Date: 2016-03-24
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Published in ParisBD 2016 : Paris Big Data Management Summit, Mar 2016, Paris, France

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02376026

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