Local-life recommendation have witnessed rapid growth, providing users with convenient access to daily essentials. However, this domain faces two key challenges: (1) spatial constraints, driven by the requirements of the local-life scenario, where items are usually shown only to users within a limited geographic area, indirectly reducing their exposure probability; and (2) long-tail sparsity, where few popular items dominate user interactions, while many high-quality long-tail items are largely overlooked due to imbalanced interaction opportunities. Existing methods typically adopt a user-centric perspective, such as modeling spatial user preferences or enhancing long-tail representations with collaborative filtering signals. However, we argue that an item-centric perspective is more suitable for this domain, focusing on enhancing long-tail items representation that align with the spatially-constrained characteristics of local lifestyle services. To tackle this issue, we propose ReST, a Plug-And-Play Spatially-Constrained Representation Enhancement Framework for Long-Tail Local-Life Recommendation. Specifically, we first introduce a Meta ID Warm-up Network, which initializes fundamental ID representations by injecting their basic attribute-level semantic information. Subsequently, we propose a novel Spatially-Constrained ID Representation Enhancement Network (SIDENet) based on contrastive learning, which incorporates two efficient strategies: a spatially-constrained hard sampling strategy and a dynamic representation alignment strategy. This design adaptively identifies weak ID representations based on their attribute-level information during training. It additionally enhances them by capturing latent item relationships within the spatially-constrained characteristics of local lifestyle services, while preserving compatibility with popular items.

翻译：本地生活推荐服务发展迅速，为用户获取日常必需品提供了便利。然而，该领域面临两大关键挑战：（1）空间约束，这是由本地生活场景的需求所驱动的，商品通常仅向有限地理区域内的用户展示，间接降低了其曝光概率；（2）长尾稀疏性，少数热门商品主导了用户交互，而大量高质量的长尾商品因交互机会不均而被严重忽视。现有方法通常采用以用户为中心的观点，例如建模用户空间偏好或利用协同过滤信号增强长尾表示。然而，我们认为以商品为中心的观点更适合该领域，其重点在于增强符合本地生活服务空间约束特性的长尾商品表示。为解决此问题，我们提出了ReST，一种用于长尾本地生活推荐的即插即用空间约束表示增强框架。具体而言，我们首先引入了一个元ID预热网络，该网络通过注入基础属性级语义信息来初始化基本ID表示。随后，我们提出了一种基于对比学习的新型空间约束ID表示增强网络（SIDENet），该网络包含两种高效策略：空间约束困难采样策略和动态表示对齐策略。该设计在训练过程中根据属性级信息自适应地识别弱ID表示，并通过捕捉本地生活服务空间约束特性中潜在的商品关系来进一步增强这些表示，同时保持与热门商品的兼容性。