Representation Learning in Low-Rank Slate-Based Recommender Systems
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.
Cite
Text
Dai and Sun. "Representation Learning in Low-Rank Slate-Based Recommender Systems." ICML 2023 Workshops: MFPL, 2023.Markdown
[Dai and Sun. "Representation Learning in Low-Rank Slate-Based Recommender Systems." ICML 2023 Workshops: MFPL, 2023.](https://mlanthology.org/icmlw/2023/dai2023icmlw-representation/)BibTeX
@inproceedings{dai2023icmlw-representation,
title = {{Representation Learning in Low-Rank Slate-Based Recommender Systems}},
author = {Dai, Yijia and Sun, Wen},
booktitle = {ICML 2023 Workshops: MFPL},
year = {2023},
url = {https://mlanthology.org/icmlw/2023/dai2023icmlw-representation/}
}