[{"id":19493,"title":"Estimation and Inference under Recommender Interference","permalink":"https:\/\/bschool.nus.edu.sg\/biz-events\/event\/estimation-and-inference-under-recommender-interference\/","category":"Seminars and talks","event_dept":{"value":"analytics-operations","label":"Analytics & Operations"},"event_sec_dept":false,"event_details":{"event_start_date":"26 January 2024","event_end_date":"26 January 2024","event_start_time":"10:00 am","event_end_time":"11:30 am","event_dress_code":"NA"},"event_loc":{"eve_address_selection":"5","eve_location_1":{"eve_org":"NUS Business School","eve_build":"Mochtar Riady Building","eve_room":"3-2","eve_add":"15 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119245,"eve_map_url":"https:\/\/goo.gl\/maps\/Q1kyjwxHNE22"},"eve_location_2":{"eve_org":"Shaw Foundation Alumni House","eve_build":"","eve_room":"Clove and Lemongrass Room Level 2","eve_add":"11 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119244,"eve_map_url":"https:\/\/goo.gl\/maps\/docgThkDWFxKdb9c7"},"eve_location_3":{"eve_org":"Hon Sui Sen Memorial Library Auditorium","eve_build":"","eve_room":"","eve_add":"1 Hon Sui Sen Drive","eve_count":"Singapore","eve_copos":117588,"eve_map_url":"https:\/\/goo.gl\/maps\/NJjWK4RMpC92"},"eve_location_4":{"eve_org":"NUSS Kent Ridge Guild House","eve_build":"","eve_room":"Dalvey Room","eve_add":"9 Kent Ridge Drive","eve_count":"Singapore","eve_copos":119241,"eve_map_url":"https:\/\/goo.gl\/maps\/nXn2Luh96pH2"},"eve_location_5":{"eve_org":"Institute of Data Science","eve_build":"Innovation 4.0","eve_room":"I4-01-03 (Level 1, Seminar Room)","eve_add":"3 Research Link","eve_count":"Singapore","eve_copos":"117602","eve_map_url":"https:\/\/goo.gl\/maps\/i1xocvvDh27QUXem7"},"eve_location_6":{"eve_org":"","eve_build":"","eve_room":"","eve_add":"","eve_count":"","eve_copos":"","eve_map_url":""},"eve_location_7":""},"event_introduction":"","event_short_intro":"","event_topic":null,"event_banner":false,"event_external_url":"","event_registration_details":{"event_registration_form":false,"event_registration_message":"","event_registration_deadline":null,"eve_registration_url":"","event_form":"","event_registration_ack":""},"event_speaker":[{"event_speaker_name":"Zhan Ruohan","event_speaker_designation":"Assistant Professor","event_speaker_affiliation":"Department of Industrial Engineering and Decision Analytics, Hong Kong University of Science and Technology","event_speaker_picture":false,"event_speaker_url":"","event_speaker_introduction":"Ruohan Zhan is an assistant professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. She earned her PhD from Stanford University and her BS from Peking University. Specializing in causal inference, statistics, and machine learning, Ruohan develops new methods to solve problems from online marketplaces, particularly on challenges related to causal effect identification, economic analysis, experimentation and operations. Her research has been published in top-tier journals including Management Science and Proceedings of National Academy of Sciences, as well as leading machine learning conferences including NeurIPS, ICLR, WWW, and KDD.<\/p>\n"}],"event_agenda":false,"event_photo_gallery":false,"event_presentations":false,"event_custom_heading":[{"event_custom_title":"Abstract","event_custom_details":"
In digital platforms, recommender systems (RecSys) are essential for aligning content with viewer preferences. This work considers the evaluation of RecSys updates, referred to as “treatments”, by analyzing their “global treatment effect” (GTE) – the expected overall benefit of universally applying these treatments. Our focus is on treatments targeting content creators. We utilize A\/B experiments on the creator side and identify that the conventional difference-in-mean estimator is biased for GTE estimation, due to interference among creators competing for visibility. To address this challenge,\u00a0we introduce a semi-parametric model that combines a parametric choice model, designed to streamline the recommendation process, with a nonparametric component that employs machine learning to account for the heterogeneity among viewers and content.\u00a0Using this model, we approximate GTE with a doubly robust estimator that satisfies\u00a0Neyman orthogonality, ensuring consistency and asymptotic\u00a0normality, and supporting hypothesis testing. We show the robustness and semiparametric efficiency of our estimator even under model mis-specification. We demonstrate the efficacy of our method through simulations and practical applications on a leading short video platform. This is joint work with Shichao Han, Yuchen Hu, and Zhenling Jiang.<\/p>\n"}],"event_enquiry_details":{"event_enq_full_name":"","event_enq_department":"","event_enq_email":"","event_enq_telephone":"","event_enq_website":""}}]