REVEAL 2019: Reinforcement and Robust Estimators for Recommendation Workshop at ACM RecSys 2019, Copenhagen, Denmark September 20, 2019 https://sites.google.com/view/reveal2019/
State-of-the-art recommender systems are notoriously hard to design and improve upon due to their interactive and dynamic nature. They involve a multi-step decision-making process, where a stream of interactions occurs between the user and the system. Leveraging reward signals from these interactions and creating a scalable and performant recommendation inference model is a key challenge. Traditionally, interactions have often been viewed as independent to make the problem tractable. But in order to improve recommender systems further, models will need to take into account the delayed effects of each recommendation and start reasoning/planning for longer-term user satisfaction. To this end, our workshop invites contributions that enable recommender systems to adapt effectively to diverse forms of user feedback and to optimize the quality of each user's long-term experience. More broadly, we invite submissions of extended abstracts (max 2 pages) on the following and related topics: - Reinforcement learning and bandits for recommendation - Robust estimators, counterfactual and off-policy evaluation - Causal recommender systems - Using simulation for recommender systems evaluation - New evaluation datasets - New offline metrics for recommender systems Accepted contributions will be presented as talks or posters. The reviews will be single-blind. Submission deadline: August 1, 2019 via https://cmt3.research.microsoft.com/REVEAL2019. Organizers: - Thorsten Joachims, Information Science and Computer Science, Cornell University - Adith Swaminathan, Deep Learning Technology Center, Microsoft Research - Maria Dimakopoulou and Yves Raimond, R&D, Netflix - Olivier Koch and Flavian Vasile, R&D, Criteo --- Thorsten Joachims Professor, Cornell University Department of Computer Science Department of Information Science http://www.joachims.org/
_______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
