Skip to Main content Skip to Navigation

Clusterisation incrémentale, multicritères de données hétérogènes pour la personnalisation d'expérience utilisateur

Abstract : In many activity sectors (health, online sales,...) designing from scratch an optimal solution for a defined problem (finding a protocol to increase the cure rate, designing a web page to promote the purchase of one or more products,...) is often very difficult or even impossible. In order to face this difficulty, designers (doctors, web designers, production engineers,...) often work incrementally by successive improvements of an existing solution. However, defining the most relevant changes remains a difficult problem. Therefore, a solution adopted more and more frequently is to compare constructively different alternatives (also called variations) in order to determine the best one by an A/B Test. The idea is to implement these alternatives and compare the results obtained, i.e. the respective rewards obtained by each variation. To identify the optimal variation in the shortest possible time, many test methods use an automated dynamic allocation strategy. Its allocate the tested subjects quickly and automatically to the most efficient variation, through a learning reinforcement algorithms (as one-armed bandit methods). These methods have shown their interest in practice but also limitations, including in particular a latency time (i.e. a delay between the arrival of a subject to be tested and its allocation) too long, a lack of explicitness of choices and the integration of an evolving context describing the subject's behaviour before being tested. The overall objective of this thesis is to propose a understable generic A/B test method allowing a dynamic real-time allocation which take into account the temporals static subjects’s characteristics.
Document type :
Complete list of metadatas

Cited literature [165 references]  Display  Hide  Download
Contributor : Emmanuelle Claeys <>
Submitted on : Saturday, January 11, 2020 - 3:11:24 AM
Last modification on : Tuesday, October 20, 2020 - 10:24:44 AM
Long-term archiving on: : Sunday, April 12, 2020 - 2:10:47 PM


Files produced by the author(s)


  • HAL Id : tel-02435605, version 1



Emmanuelle Claeys. Clusterisation incrémentale, multicritères de données hétérogènes pour la personnalisation d'expérience utilisateur. Intelligence artificielle [cs.AI]. Université de strasbourg, 2019. Français. ⟨tel-02435605⟩



Record views


Files downloads