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Representation Learning in Low-rank Slate-based Recommender Systems
Yijia Dai · Wen Sun

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in The Many Facets of Preference-Based Learning »
Event URL: https://openreview.net/forum?id=hy6EVQWNuU »

Reinforcement learning (RL) in recommendation systems offers the potential to optimize recommendations for long-term user engagement. However, the environment often involves large state and action spaces, which makes it hard to efficiently learn and do efficient exploration. In this work, we propose a sample-efficient representation learning algorithm, using the standard slate recommendation setup, to treat this as an online RL problem with low-rank Markov decision processes (MDPs). We also construct the recommender simulation environment with the proposed setup and sampling method.

Author Information

Yijia Dai (Cornell University)
Wen Sun (Cornell University)

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