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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 the agent naturally captures manual exploration by human analysts, and succeeds to learn an interpretable and transferable exploration policy.<\/jats:p>","DOI":"10.14778\/3397230.3397242","type":"journal-article","created":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T11:46:24Z","timestamp":1593431184000},"page":"1469-1482","source":"Crossref","is-referenced-by-count":16,"title":["Guided exploration of user groups"],"prefix":"10.14778","volume":"13","author":[{"given":"Mariia","family":"Seleznova","sequence":"first","affiliation":[{"name":"TU Berlin"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Behrooz","family":"Omidvar-Tehrani","sequence":"additional","affiliation":[{"name":"NAVER LABS Europe"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sihem","family":"Amer-Yahia","sequence":"additional","affiliation":[{"name":"CNRS, University of Grenoble Alpes"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Simon","sequence":"additional","affiliation":[{"name":"SAP Paris"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,6,26]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2913651"},{"key":"e_1_2_1_2_1","unstructured":"Qualtrics Marketplace (SAP). https:\/\/www.qualtrics.com\/marketplace\/.  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