  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.
