| Autor: |
Ritesh Agarwal, Craig Boutilier, Sanmit Narvekar, Eugene Ie, Jing Wang, Rui Wu, Vihan Jain, Tushar Deepak Chandra, Heng-Tze Cheng |
| Rok vydání: |
2019 |
| Předmět: |
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| Zdroj: |
IJCAI |
| DOI: |
10.24963/ijcai.2019/360 |
| Popis: |
Reinforcement learning methods for recommender systems optimize recommendations for long-term user engagement. However, since users are often presented with slates of multiple items---which may have interacting effects on user choice---methods are required to deal with the combinatorics of the RL action space. We develop SlateQ, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates. Under mild assumptions on user choice behavior, we show that the long-term value (LTV) of a slate can be decomposed into a tractable function of its component item-wise LTVs. We demonstrate our methods in simulation, and validate the scalability and effectiveness of decomposed TD-learning on YouTube. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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