Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation

• URL: http://arxiv.org/abs/2409.07416v1
• Date: Wed, 11 Sep 2024 17:01:06 GMT
• Title: Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation
• Authors: Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang, Jingren Zhou,
• Abstract summary: 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. Results observe significant performance improvement by our method, compared with several well-known baselines.
• Score: 51.06031200728449
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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