ABSTRACT
The REVEAL workshop1 focuses on framing the recommendation problem as a one of making personalized interventions, e.g. deciding to recommend a particular item to a particular user. Moreover, these interventions sometimes depend on each other, where a stream of interactions occurs between the user and the system, and where each decision to recommend something will have an impact on future steps and long-term rewards. This framing creates a number of challenges we will discuss at the workshop. How can recommender systems be evaluated offline in such a context? How can we learn recommendation policies that are aware of these delayed consequences and outcomes?
- REVEAL 2020: Bandit and Reinforcement Learning from User Interactions
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REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation
RecSys '19: Proceedings of the 13th ACM Conference on Recommender SystemsThe REVEAL workshop1 focuses on framing the recommendation problem as a one of making personalized interventions. Moreover, these interventions sometimes depend on each other, where a stream of interactions occurs between the user and the system, and ...
REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale
RecSys '22: Proceedings of the 16th ACM Conference on Recommender SystemsRecommendation systems are increasingly modelled as a sequential decision making process, where the system decides which items to recommend to a given user. Each decision to recommend an item or slate of items has a significant impact on immediate and ...
Context-aware reinforcement learning for course recommendation
AbstractOnline course recommendation is an extremely relevant ingredient for the efficiency of e-learning. The current recommendation methods cannot guarantee the effectiveness and accuracy of course recommendation, especially when a user has enrolled in ...
Highlights- We present a context-aware reinforcement learning model for course recommendation.
- A novel interaction improves the effectiveness of course recommendation.
- We propose a contextual policy gradient method with approximation for RRL.
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