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Seattle, Washington
Conference
RecSys 2022
September 18 - 23, 2022
Seattle, Washington

Overview

The ACM Recommender Systems conference (RecSys) is the premier international forum for the presentation of new research results, systems, and techniques in the broad field of recommender systems. RecSys brings together the major international research groups working on recommender systems, along with many of the world’s leading companies active in e-commerce and other adjacent domains.

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Amazon organizing committee members

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Workshops

RecSys 2022 Workshop: CONSEQUENCES – Causality, Counterfactuals and Sequential Decision-Making
Unknown date
The CONSEQUENCES+REVEAL workshop is an in-person event co-located with the Sixteenth ACM Conference on Recommender Systems in Seattle, WA, USA.

Amazon organizer: Olivier Jeunen

Website: https://sites.google.com/view/consequences2022
Recsys 2022 Workshop on Knowledge-aware and Conversational Recommender Systems
September 23
A nonparametric contextual bandit with arm-level eligibility control for customer service routing
Ruofeng Wen, Wenjun Zeng, Yi Liu
2022
Machine learning
Recsys 2022 Workshop on Multi-Objective Recommender Systems
September 23
Mitigating targeting bias in content recommendation with causal bandits'
Yan Zhao, Mitchell Goodman, Sameer Kanase, Shenghe Xu, Yannick Kimmel, Brent Payne, Saad Khan, Patricia Grao
2022
Machine learning
Recsys 2022 Workshop on Online Recommender Systems and User Modeling
September 23
The objective of this workshop is to foster contributions and bring together a growing community of researchers and practitioners interested in online, adaptive approaches to user modeling, recommendation and personalization, as well as other related tasks, such as evaluation, reproducibility, privacy and explainability.

Amazon keynote speaker: Eugene Yan

Amazon program committee: Olivier Jeunen

Website: https://orsum.inesctec.pt/orsum2022
An application of causal bandit to content optimization
Sameer Kanase, Yan Zhao, Shenghe Xu, Mitchell Goodman, Manohar Mandalapu, Benjamyn Ward, Chan Jeon, Shreya Kamath, Ben Cohen, Yujia Liu, Hengjia Zhang, Yannick Kimmel, Saad Khan, Brent Payne, Patricia Grao
2022
Machine learning

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