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.

翻译：本地生活服务推荐因其强烈的日常生活需求驱动特性，与一般推荐场景存在显著差异。根本上，准确识别用户的即时生活需求并推荐相应服务是两项密不可分的任务。然而，先前的工作通常孤立地处理它们，未能实现对需求预测与服务推荐的统一建模。本文提出了一种新颖的基于大型语言模型的框架，该框架联合执行生活需求预测与服务推荐。为应对原始消费数据中的噪声挑战，我们引入了一种行为聚类方法，过滤偶然因素并选择性保留典型模式。这使得模型能够学习稳健的需求生成逻辑基础，并自发泛化至长尾场景。为探索由多样化需求、商家及复杂映射路径导致的巨大搜索空间，我们采用课程学习策略结合具有可验证奖励的强化学习。该方法引导模型依次学习从需求生成到类别映射及具体服务选择的逻辑。大量实验表明，我们的统一框架显著提升了生活需求预测性能与推荐准确性，验证了联合建模生活需求与用户行为的有效性。