Representation Learning in Low-rank Slate-based Recommender Systems

Yijia Dai, Wen Sun

Keywords: Slated-based Recommender Systems, Sample Complexity, Low-rank MDPs

TL;DR: We propose a sample-efficient representation learning recommender algorithm with slate recommendation setup, to treat it as an online RL problem with low-rank Markov decision processes.

Abstract: 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 explore. 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.

Submission Number: 46