{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T21:16:22Z","timestamp":1780607782289,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":12,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,7,7]]},"DOI":"10.1145\/3340631.3398666","type":"proceedings-article","created":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T21:49:55Z","timestamp":1594676995000},"page":"392-393","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["A Gentle Introduction to Recommendation as Counterfactual Policy Learning"],"prefix":"10.1145","author":[{"given":"Flavian","family":"Vasile","sequence":"first","affiliation":[{"name":"Criteo AI Lab, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Rohde","sequence":"additional","affiliation":[{"name":"Criteo AI Lab, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Jeunen","sequence":"additional","affiliation":[{"name":"University of Antwerp, Antwerp, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amine","family":"Benhalloum","sequence":"additional","affiliation":[{"name":"Criteo AI Lab, Paris, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"1","article-title":"2013. Counterfactual reasoning and learning systems: The example of computational advertising","volume":"14","author":"Bottou L.","year":"2013","unstructured":"L. Bottou , J. Peters , J. Qui nonero- Candela , D. Charles , D. Chickering , E. Portugaly , D. Ray , P. Simard , and E. Snelson . 2013. Counterfactual reasoning and learning systems: The example of computational advertising . The Journal of Machine Learning Research , Vol. 14 , 1 ( 2013 ), 3207--3260. L. Bottou, J. Peters, J. Qui nonero-Candela, D. Charles, D. Chickering, E. Portugaly, D. Ray, P. Simard, and E. Snelson. 2013. Counterfactual reasoning and learning systems: The example of computational advertising. The Journal of Machine Learning Research, Vol. 14, 1 (2013), 3207--3260.","journal-title":"The Journal of Machine Learning Research"},{"key":"e_1_3_2_2_2_1","volume-title":"Proc. of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM '18)","author":"Gilotte A.","unstructured":"A. Gilotte , C. Calauz\u00e8nes , T. Nedelec , A. Abraham , and S. Doll\u00e9 . 2018. Offline A\/B Testing for Recommender Systems . In Proc. of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM '18) . ACM, 198--206. A. Gilotte, C. Calauz\u00e8nes, T. Nedelec, A. Abraham, and S. Doll\u00e9. 2018. Offline A\/B Testing for Recommender Systems. In Proc. of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM '18). ACM, 198--206."},{"key":"e_1_3_2_2_3_1","volume-title":"Proceedings of the 12th ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM '19)","author":"Gruson A.","unstructured":"A. Gruson , P. Chandar , C. Charbuillet , J. McInerney , S. Hansen , D. Tardieu , and B. Carterette . 2019. Offline Evaluation to Make Decisions About Playlist Recommendation Algorithms . In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM '19) . ACM, New York, NY, USA, 420--428. A. Gruson, P. Chandar, C. Charbuillet, J. McInerney, S. Hansen, D. Tardieu, and B. Carterette. 2019. Offline Evaluation to Make Decisions About Playlist Recommendation Algorithms. In Proceedings of the 12th ACM International Conference on Web Search and Data Mining (Melbourne VIC, Australia) (WSDM '19). ACM, New York, NY, USA, 420--428."},{"key":"e_1_3_2_2_4_1","unstructured":"E. Ie C. Hsu M. Mladenov V. Jain S. Narvekar J. Wang R. Wu and C. Boutilier. 2019. RecSim: A Configurable Simulation Platform for Recommender Systems. arxiv: 1909.04847 [cs.LG]  E. Ie C. Hsu M. Mladenov V. Jain S. Narvekar J. Wang R. Wu and C. Boutilier. 2019. RecSim: A Configurable Simulation Platform for Recommender Systems. arxiv: 1909.04847 [cs.LG]"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3347069"},{"key":"e_1_3_2_2_6_1","volume-title":"2019 a. Learning from Bandit Feedback: An Overview of the State-of-the-art. arxiv","author":"Jeunen O.","year":"1909","unstructured":"O. Jeunen , D. Mykhaylov , D. Rohde , F. Vasile , A. Gilotte , and M. Bompaire . 2019 a. Learning from Bandit Feedback: An Overview of the State-of-the-art. arxiv : 1909 .08471 [cs.IR] O. Jeunen, D. Mykhaylov, D. Rohde, F. Vasile, A. Gilotte, and M. Bompaire. 2019 a. Learning from Bandit Feedback: An Overview of the State-of-the-art. arxiv: 1909.08471 [cs.IR]"},{"key":"e_1_3_2_2_7_1","volume-title":"2019 b. On the Value of Bandit Feedback for Offline Recommender System Evaluation. arxiv","author":"Jeunen O.","year":"1907","unstructured":"O. Jeunen , D. Rohde , and F. Vasile . 2019 b. On the Value of Bandit Feedback for Offline Recommender System Evaluation. arxiv : 1907 .12384 [cs.IR] O. Jeunen, D. Rohde, and F. Vasile. 2019 b. On the Value of Bandit Feedback for Offline Recommender System Evaluation. arxiv: 1907.12384 [cs.IR]"},{"key":"e_1_3_2_2_8_1","volume-title":"Proc. of the 6th International Conference on Learning Representations (ICLR '18)","author":"Joachims T.","year":"2018","unstructured":"T. Joachims , A. Swaminathan , and M. de Rijke . 2018 . Deep Learning with Logged Bandit Feedback . In Proc. of the 6th International Conference on Learning Representations (ICLR '18) . T. Joachims, A. Swaminathan, and M. de Rijke. 2018. Deep Learning with Logged Bandit Feedback. In Proc. of the 6th International Conference on Learning Representations (ICLR '18)."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2488200"},{"key":"e_1_3_2_2_10_1","unstructured":"D. Mykhaylov D. Rohde F. Vasile M. Bompaire and O. Jeunen. 2019. Three Methods for Training on Bandit Feedback. arxiv: 1904.10799 [cs.IR]  D. Mykhaylov D. Rohde F. Vasile M. Bompaire and O. Jeunen. 2019. Three Methods for Training on Bandit Feedback. arxiv: 1904.10799 [cs.IR]"},{"key":"e_1_3_2_2_11_1","unstructured":"D. Rohde S. Bonner T. Dunlop F. Vasile and A. Karatzoglou. 2018. RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. ArXiv e-prints (Aug. 2018). arxiv: 1808.00720 [cs.IR]  D. Rohde S. Bonner T. Dunlop F. Vasile and A. Karatzoglou. 2018. RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. ArXiv e-prints (Aug. 2018). arxiv: 1808.00720 [cs.IR]"},{"key":"e_1_3_2_2_12_1","volume-title":"Proc. of the 32nd International Conference on International Conference on Machine Learning (ICML'15)","author":"Swaminathan A.","unstructured":"A. Swaminathan and T. Joachims . 2015. Counterfactual Risk Minimization: Learning from Logged Bandit Feedback . In Proc. of the 32nd International Conference on International Conference on Machine Learning (ICML'15) . JMLR.org, 814--823. A. Swaminathan and T. Joachims. 2015. Counterfactual Risk Minimization: Learning from Logged Bandit Feedback. In Proc. of the 32nd International Conference on International Conference on Machine Learning (ICML'15). JMLR.org, 814--823."}],"event":{"name":"UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization","location":"Genoa Italy","acronym":"UMAP '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340631.3398666","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340631.3398666","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:11Z","timestamp":1750200071000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340631.3398666"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":12,"alternative-id":["10.1145\/3340631.3398666","10.1145\/3340631"],"URL":"https:\/\/doi.org\/10.1145\/3340631.3398666","relation":{},"subject":[],"published":{"date-parts":[[2020,7,7]]},"assertion":[{"value":"2020-07-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}