Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
<|reference_start|>Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation: Modern listwise recommendation systems need to consider both long-term user perceptions and short-term interest shifts. Reinforcement learning can be applied on recommendation to study such a problem but is also subject to large search space, sparse user feedback and long interactive latency. Motivated by recent progress in hierarchical reinforcement learning, we propose a novel framework called mccHRL to provide different levels of temporal abstraction on listwise recommendation. Within the hierarchical framework, the high-level agent studies the evolution of user perception, while the low-level agent produces the item selection policy by modeling the process as a sequential decision-making problem. We argue that such framework has a well-defined decomposition of the outra-session context and the intra-session context, which are encoded by the high-level and low-level agents, respectively. To verify this argument, we implement both a simulator-based environment and an industrial dataset-based experiment. Results observe significant performance improvement by our method, compared with several well-known baselines. Data and codes have been made public.<|reference_end|>
@article{ji2024hierarchical,
    title={Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise
  Recommendation},
    author={Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang, Jingren Zhou},
    journal={arXiv preprint arXiv:2409.07416},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.07416},
    primaryClass={cs.IR cs.AI cs.LG}
}