AI 中文总结
该研究提出感知天气与位置的智能体餐饮推荐系统，利用LLM的世界知识生成区域敏感的适配天气的餐饮推荐，为智能体推荐提供了可扩展的架构模式。
AI 中文摘要
语境感知推荐系统早已认识到位置、时间、天气等因素会影响人们选择饮食的地点与内容。然而，现有的感知天气的食品及兴趣点推荐器通常将天气视为通用因素，通过手工规则或专门训练的语境模型将天气状况映射到偏好，却未捕捉到对天气的文化适配反应本身具有区域特异性：在一种饮食文化中，雨天夜晚适合饮用热茶、食用油炸小吃，而在另一种文化中则需要截然不同的慰藉食物。将这种天气-区域-饮食的交互编码为显式规则或训练数据既脆弱又无法扩展。我们提出了一种感知天气与位置的智能体餐饮推荐系统，采用不同方法：大语言模型（LLM）编排位置与天气检索工具，随后基于组合语境进行自然语言推理，利用模型中已潜藏的文化与饮食世界知识生成区域敏感、适配天气的推荐，无需针对各区域的规则表或专门训练。我们描述了智能体架构、工具编排流程（谷歌位置服务与天气服务为OpenAI LLM提供数据）及推理机制，并报告了已实现并进行端到端简短部署的工作原型。我们讨论了设计权衡（成本、延迟、歧义处理与备选方案），并明确指出了局限性，包括缺乏正式用户研究，以及基于区域推理可能存在的文化刻板印象风险。贡献在于架构：一种简单、可扩展的模式，通过LLM推理而非工程化规则将环境与文化语境融入智能体推荐。
英文摘要
Context-aware recommender systems have long recognized that factors such as location, time, and weather shape where and what people choose to eat. Existing weather-aware food and point-of-interest recommenders, however, typically treat weather generically -- mapping conditions to preferences through hand-crafted rules or specially trained context models -- and do not capture that the culturally appropriate response to weather is itself region-specific: a rainy evening calls for hot tea and fried snacks in one culinary culture and for very different comfort food in another. Encoding such weather-by-region-by-cuisine interactions as explicit rules or training data is brittle and does not scale. We present a weather- and location-aware agentic dining-recommendation system that takes a different approach: a large language model (LLM) orchestrates tools for location and weather retrieval and then reasons in natural language over the combined context, drawing on the cultural and culinary world knowledge already latent in the model to produce region-sensitive, weather-appropriate recommendations without per-region rule tables or specialized training. We describe the agent architecture, the tool-orchestration flow (Google location services and a weather service feeding an OpenAI LLM), and the reasoning mechanism, and we report on a working prototype that was implemented and briefly deployed end-to-end. We discuss design trade-offs -- cost, latency, ambiguity handling, and fallbacks -- and we are explicit about limitations, including the absence of a formal user study and the risk of cultural stereotyping in locality-based inference. The contribution is architectural: a simple, extensible pattern for incorporating environmental and cultural context into agentic recommendation through LLM reasoning rather than engineered rules.
Comments5 pages. An agentic LLM system that reasons over combined location and weather context for region-sensitive dining recommendation. Working prototype implemented and briefly deployed end-to-end
