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Communication Dans Un Congrès Année : 2016

Uplift modeling for recommendation system

Résumé

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.
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Dates et versions

hal-02376026 , version 1 (22-11-2019)

Identifiants

  • HAL Id : hal-02376026 , version 1

Citer

Atef Shaar, Talel Abdessalem, Olivier Segard. Uplift modeling for recommendation system. ParisBD 2016 : Paris Big Data Management Summit, Mar 2016, Paris, France. ⟨hal-02376026⟩
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