Abstract
Mariia Seleznova, Bernard Omidvar-Tehrani, Sihem Amer-Yahia, Eric Simon |
46th International Conference on Very Large Data Bases (VLDB), Tokyo, Japan, 31 August-4 September, 2020 |
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Abstract
Finding a set of users of interest serves several applications in behavioral analytics. Often times, identifying users requires to explore the data and gradually choose potential target. This is a special case of Exploratory Data Analysis (EDA), an iterative and tedious process. In this paper, we formalize and solve the problem of guided exploration of user groups whose purpose is to find target users. We model exploration as an iterative decision-making process, where an agent is shown a set of groups, chooses users from those groups, and selects the best action to move to the next step. To solve our problem, we apply reinforcement learning to discover an efficient exploration strategy from a simulated agent experience, and propose to use the learned strategy to recommend an exploration policy that can be applied to the same task for any dataset. Our framework accepts a wide class of exploration actions and does not need to gather exploration logs. Our experiments show that theagent naturally captures manual exploration by human analysts, and succeeds to learn an interpretable and transferable exploration policy.
Also presented at the French Data Management Conference BDA 2020, Paris, France, 27-30 October 2020
Details on the gender equality index score 2023 (related to year 2022) for NAVER France of 81/100.
NAVER France targets are as follows:
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Index NAVER France de l’égalité professionnelle entre les femmes et les hommes pour l’année 2023 au titre des données 2022 : 81/100
Détail des indicateurs :
Les objectifs de progression de NAVER France sont :
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