{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T20:10:18Z","timestamp":1768421418559,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,9,22]],"date-time":"2020-09-22T00:00:00Z","timestamp":1600732800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,22]]},"DOI":"10.1145\/3383313.3411527","type":"proceedings-article","created":{"date-parts":[[2020,9,19]],"date-time":"2020-09-19T02:28:22Z","timestamp":1600482502000},"page":"591-593","source":"Crossref","is-referenced-by-count":21,"title":["Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NG"],"prefix":"10.1145","author":[{"given":"Martin","family":"Mladenov","sequence":"first","affiliation":[{"name":"Google Research, United States"}]},{"given":"Chih-wei","family":"Hsu","sequence":"additional","affiliation":[{"name":"Google Research, USA"}]},{"given":"Vihan","family":"Jain","sequence":"additional","affiliation":[{"name":"Google Research, United States"}]},{"given":"Eugene","family":"Ie","sequence":"additional","affiliation":[{"name":"Google Research, United States"}]},{"given":"Christopher","family":"Colby","sequence":"additional","affiliation":[{"name":"Google Research, United States"}]},{"given":"Nicolas","family":"Mayoraz","sequence":"additional","affiliation":[{"name":"Google Research, United States"}]},{"given":"Hubert","family":"Pham","sequence":"additional","affiliation":[{"name":"Google Research, United States"}]},{"given":"Dustin","family":"Tran","sequence":"additional","affiliation":[{"name":"Google Brain, United States"}]},{"given":"Ivan","family":"Vendrov","sequence":"additional","affiliation":[{"name":"Google Research, United States"}]},{"given":"Craig","family":"Boutilier","sequence":"additional","affiliation":[{"name":"Google Research, United States"}]}],"member":"320","published-online":{"date-parts":[[2020,9,22]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"WES: Agent-based User Interaction Simulation on Real Infrastructure. arXiv:2004.05363.","author":"Ahlgren M.","year":"2020"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.3912"},{"key":"e_1_3_2_1_3_1","unstructured":"C.\u00a0Boutilier C.-w.\u00a0Hsu B.\u00a0Kveton M.\u00a0Mladenov C.\u00a0Szepesvari and M.\u00a0Zaheer. 2020. Differentiable Bandit Exploration. (2020). arXiv:2002.06772.  C.\u00a0Boutilier C.-w.\u00a0Hsu B.\u00a0Kveton M.\u00a0Mladenov C.\u00a0Szepesvari and M.\u00a0Zaheer. 2020. Differentiable Bandit Exploration. (2020). arXiv:2002.06772."},{"key":"e_1_3_2_1_4_1","volume-title":"arXiv:arXiv:1606.01540preprint arXiv:1606.01540","author":"Brockman V.","year":"2016"},{"key":"e_1_3_2_1_5_1","volume-title":"Dopamine: A Research Framework for Deep Reinforcement Learning.","author":"Castro S.","year":"2018"},{"key":"e_1_3_2_1_6_1","volume-title":"Music Recommendation and Discovery","author":"Celma 0."},{"key":"e_1_3_2_1_7_1","volume-title":"12th ACM International Conference on Web Search and Data Mining (WSDM-19)","author":"Chen A.","year":"2018"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975321.69"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939746"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.1989.tb00324.x"},{"key":"e_1_3_2_1_11_1","volume-title":"Horizon: Facebook\u2019s Open Source Applied Reinforcement Learning Platform.","author":"Gauci E.","year":"2018"},{"key":"e_1_3_2_1_12_1","volume-title":"TF-Agents: A Library for Reinforcement Learning in TensorFlow. https:\/\/github.com\/tensorflow\/agents. https:\/\/github.com\/tensorflow\/agents Online","author":"Guadarrama A.","year":"2019"},{"key":"e_1_3_2_1_13_1","volume-title":"SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets. In International Joint Conference on Artifical Intelligence (IJCAI). Macau, 2592\u20132599","author":"Ie V.","year":"2019"},{"key":"e_1_3_2_1_14_1","unstructured":"E.\u00a0Ie C.-w.\u00a0Hsu M.\u00a0Mladenov V.\u00a0Jain S.\u00a0Narvekar J.\u00a0Wang R.\u00a0Wu and C.\u00a0Boutilier. 2019. RecSim: A Configurable Simulation Platform for Recommender Systems. (2019). arXiv:1909.04847.  E.\u00a0Ie C.-w.\u00a0Hsu M.\u00a0Mladenov V.\u00a0Jain S.\u00a0Narvekar J.\u00a0Wang R.\u00a0Wu and C.\u00a0Boutilier. 2019. RecSim: A Configurable Simulation Platform for Recommender Systems. (2019). arXiv:1909.04847."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/2073876.2073912"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2911451.2911548"},{"key":"e_1_3_2_1_17_1","volume-title":"Proceedings of the Thirty-seventh International Conference on Machine Learning (ICML-20)","author":"Mladenov E.","year":"2020"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"M.\u00a0Mladenov C.-w.\u00a0Hsu V.\u00a0Jain E.\u00a0Ie C.\u00a0Colby N.\u00a0Mayoraz H.\u00a0Pham D.\u00a0Tran I.\u00a0Vendrov and C.\u00a0Boutilier. 2020. RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems. (2020). in preparation.  M.\u00a0Mladenov C.-w.\u00a0Hsu V.\u00a0Jain E.\u00a0Ie C.\u00a0Colby N.\u00a0Mayoraz H.\u00a0Pham D.\u00a0Tran I.\u00a0Vendrov and C.\u00a0Boutilier. 2020. RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems. (2020). in preparation.","DOI":"10.1145\/3383313.3411527"},{"key":"e_1_3_2_1_19_1","volume-title":"Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97)","author":"Pfeffer D.","year":"1997"},{"key":"e_1_3_2_1_20_1","unstructured":"D.\u00a0Rohde S.\u00a0Bonner T.\u00a0Dunlop F.\u00a0Vasile and A.\u00a0Karatzoglou. 2018. RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. (2018). arXiv:1808.00720 [cs.IR].  D.\u00a0Rohde S.\u00a0Bonner T.\u00a0Dunlop F.\u00a0Vasile and A.\u00a0Karatzoglou. 2018. RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. (2018). arXiv:1808.00720 [cs.IR]."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/1046920.1088715"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014902"},{"key":"e_1_3_2_1_23_1","unstructured":"Y.\u00a0Sun and Y.\u00a0Zhang. 2018. Conversational Recommender System. (2018). arXiv:1806.03277 [cs.IR].  Y.\u00a0Sun and Y.\u00a0Zhang. 2018. Conversational Recommender System. (2018). arXiv:1806.03277 [cs.IR]."},{"key":"e_1_3_2_1_24_1","volume-title":"ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games. In Advances in Neural Information Processing Systems 30 (NIPS-17).","author":"Yuandong Tian Q.","year":"2017"},{"key":"e_1_3_2_1_25_1","unstructured":"Dustin Tran M.\u00a0W. Hoffman D.\u00a0Moore C.\u00a0Suter S.\u00a0Vasudevan and A.\u00a0Radul. 2018. Simple distributed and accelerated probabilistic programming. In Advances in Neural Information Processing Systems 31 (NeurIPS-18). Montreal 7598\u20137609.  Dustin Tran M.\u00a0W. Hoffman D.\u00a0Moore C.\u00a0Suter S.\u00a0Vasudevan and A.\u00a0Radul. 2018. Simple distributed and accelerated probabilistic programming. In Advances in Neural Information Processing Systems 31 (NeurIPS-18). Montreal 7598\u20137609."},{"key":"e_1_3_2_1_26_1","unstructured":"X.\u00a0Zhao L.\u00a0Xia Z.\u00a0Ding D.\u00a0Yin and J.\u00a0Tang. 2019. Toward Simulating Environments in Reinforcement Learning Based Recommendations. (2019). arXiv:1906.11462.  X.\u00a0Zhao L.\u00a0Xia Z.\u00a0Ding D.\u00a0Yin and J.\u00a0Tang. 2019. Toward Simulating Environments in Reinforcement Learning Based Recommendations. (2019). arXiv:1906.11462."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3240323.3240374"},{"key":"e_1_3_2_1_28_1","unstructured":"Stephan Zheng A.\u00a0Trott S.\u00a0Srinivasa N.\u00a0Naik M.\u00a0Gruesbeck D.\u00a0C. Parkes and R.\u00a0Socher. 2020. The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies. arXiv:2004.13332.  Stephan Zheng A.\u00a0Trott S.\u00a0Srinivasa N.\u00a0Naik M.\u00a0Gruesbeck D.\u00a0C. Parkes and R.\u00a0Socher. 2020. The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies. arXiv:2004.13332."}],"event":{"name":"RecSys '20: Fourteenth ACM Conference on Recommender Systems","location":"Virtual Event Brazil","acronym":"RecSys '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval","SIGCHI ACM Special Interest Group on Computer-Human Interaction","SIGecom Special Interest Group on Economics and Computation"]},"container-title":["Fourteenth ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3383313.3411527","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3383313.3411527","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:02:02Z","timestamp":1750197722000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3383313.3411527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,22]]},"references-count":28,"alternative-id":["10.1145\/3383313.3411527","10.1145\/3383313"],"URL":"https:\/\/doi.org\/10.1145\/3383313.3411527","relation":{},"subject":[],"published":{"date-parts":[[2020,9,22]]}}}