mlsanityData to 14 Oct 2026

Representation Learning in Low-rank Slate-based Recommender Systems

arXiv 2309.08622 ↗First posted 10 Sep 2023cs.IR cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 40 references that are in the map. First posted on arXiv 10 Sep 2023.

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15 of 40 references.

References · 15

  1. 2019Provably Efficient Reinforcement Learning with Linear Function Approximationcited by 520
  2. 2020S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximizationcited by 311
  3. 2016Recommendations as Treatments: Debiasing Learning and Evaluationcited by 248
  4. 2020FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPscited by 233
  5. 2021Reinforcement learning based recommender systems: A surveycited by 188
  6. 2016Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendationcited by 152
  7. 2021Representation Learning for Online and Offline RL in Low-rank MDPscited by 129
  8. 2019Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learningcited by 114
  9. 2016Off-policy evaluation for slate recommendationcited by 103
  10. 2020Self-Supervised Reinforcement Learning for Recommender Systemscited by 49
  11. 2018Horizon: Facebook's Open Source Applied Reinforcement Learning Platformcited by 48
  12. 2013Empirical Analysis of Predictive Algorithms for Collaborative Filteringcited by 47
3 more
  1. 2022Provable Reinforcement Learning with a Short-Term Memorycited by 40
  2. 2019Reinforcement Learning for Slate-based Recommender Systems: A Tractable Decomposition and Practical Methodologycited by 27
  3. 2021RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystemscited by 21

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