The content discusses the importance of incorporating planning capabilities into recommendation systems to address long-term engagement issues. The proposed Bi-level Learnable LLM Planner framework leverages large language models for personalized recommendations, showcasing superior performance in learning to plan for long-term recommendations.
Traditional recommendation systems tend to focus on immediate interests, neglecting long-term engagement. Reinforcement Learning struggles with sparse data, leading to suboptimal performance in planning. Large Language Models offer powerful planning capabilities through pre-training on diverse textual data.
The proposed Bi-level Learnable LLM Planner framework integrates macro-learning and micro-learning mechanisms for enhanced planning in personalized recommendations. Extensive experiments validate the framework's superiority in learning to plan for long-term recommendations.
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