Large Language Models are Learnable Planners for Long-Term Recommendation
<|reference_start|>Large Language Models are Learnable Planners for Long-Term Recommendation: Planning for both immediate and long-term benefits becomes increasingly important in recommendation. Existing methods apply Reinforcement Learning (RL) to learn planning capacity by maximizing cumulative reward for long-term recommendation. However, the scarcity of recommendation data presents challenges such as instability and susceptibility to overfitting when training RL models from scratch, resulting in sub-optimal performance. In this light, we propose to leverage the remarkable planning capabilities over sparse data of Large Language Models (LLMs) for long-term recommendation. The key to achieving the target lies in formulating a guidance plan following principles of enhancing long-term engagement and grounding the plan to effective and executable actions in a personalized manner. To this end, we propose a Bi-level Learnable LLM Planner framework, which consists of a set of LLM instances and breaks down the learning process into macro-learning and micro-learning to learn macro-level guidance and micro-level personalized recommendation policies, respectively. Extensive experiments validate that the framework facilitates the planning ability of LLMs for long-term recommendation. Our code and data can be found at https://github.com/jizhi-zhang/BiLLP.<|reference_end|>
@article{shi2024large,
    title={Large Language Models are Learnable Planners for Long-Term
  Recommendation},
    author={Wentao Shi, Xiangnan He, Yang Zhang, Chongming Gao, Xinyue Li, Jizhi
  Zhang, Qifan Wang, Fuli Feng},
    journal={arXiv preprint arXiv:2403.00843},
    year={2024},
    doi={10.1145/3626772.3657683},
    archivePrefix={arXiv},
    eprint={2403.00843},
    primaryClass={cs.IR cs.AI cs.CL cs.LG}
}