Enhancing Local Life Service Recommendation with Agentic Reasoning in Large Language Model
For local life service platforms, this work provides a unified approach to need-driven recommendation, but the novelty is incremental as it combines existing techniques (behavioral clustering, curriculum learning, RL) with LLMs.
The paper proposes a unified LLM-based framework that jointly predicts users' immediate living needs and recommends local services, addressing the limitation of prior works that treat these tasks separately. Experiments show significant improvements in both need prediction and recommendation accuracy.
Local life service recommendation is distinct from general recommendation scenarios due to its strong living need-driven nature. Fundamentally, accurately identifying a user's immediate living need and recommending the corresponding service are inextricably linked tasks. However, prior works typically treat them in isolation, failing to achieve a unified modeling of need prediction and service recommendation. In this paper, we propose a novel large language model based framework that jointly performs living need prediction and service recommendation. To address the challenge of noise in raw consumption data, we introduce a behavioral clustering approach that filters out accidental factors and selectively preserves typical patterns. This enables the model to learn a robust logical basis for need generation and spontaneously generalize to long-tail scenarios. To navigate the vast search space stemming from diverse needs, merchants, and complex mapping paths, we employ a curriculum learning strategy combined with reinforcement learning with verifiable rewards. This approach guides the model to sequentially learn the logic from need generation to category mapping and specific service selection. Extensive experiments demonstrate that our unified framework significantly enhances both living need prediction performance and recommendation accuracy, validating the effectiveness of jointly modeling living needs and user behaviors.