发表机构
Georgia Institute of Technology; The University of Texas at Austin（佐治亚理工学院; 德克萨斯大学奥斯汀分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对重复订单食品配送推荐，提出MARS模块化多智能体重排框架，分两阶段推荐，结合多种信号与推理。通过实验评估，贡献包括给出集成框架、证明预训练主干结合轻量级检索有竞争力、建立可重现评估设置。
AI 中文摘要
大语言模型（LLMs）在推荐系统中应用日益广泛，但在结构化推荐管道中单独使用强大的预训练主干能获得多少性能尚不清楚。本文提出MARS，一个用于重复订单食品配送推荐的模块化多智能体重排框架。它分菜肴预测和供应商排名两阶段进行粗到细的推荐，结合多种信号和推理。在两个真实基准上评估并与多种基线比较，还提供详细协议。研究有三点贡献：提出集成协作信号和基于LLM重排的框架；表明预训练主干与轻量级协作检索结合有竞争力；为食品配送混合LLM推荐建立可重现评估设置。
英文摘要
Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pre-trained backbones alone when they are placed inside a structured recommendation pipeline. In this paper, we present MARS, a modular multi-agent re-ranking framework for repeat-order food delivery recommendation. MARS serves as a controlled hybrid framework for studying how far pre-trained LLMs can go in this setting when combined with lightweight collaborative retrieval and contextual filtering. MARS performs coarse-to-fine recommendation in two stages: cuisine prediction followed by vendor ranking. The framework combines LightGCN-based global preference signals, Swing-based local peer evidence, geospatial filtering, and prompt-driven LLM reasoning over behavioral, temporal, and geographic context. We evaluate MARS on two real-world Delivery Hero benchmarks, DHRD-SE and DHRD-SG, and compare it against heuristic, sequential, graph-based, and food-delivery-specific baselines. We also provide detailed implementation and evaluation protocols, including prompting and decoding. Our study makes three contributions. First, it presents a modular multi-agent framework for repeat-order food delivery recommendation that integrates collaborative signals and LLM-based re-ranking in a transparent pipeline. Second, it shows that strong pre-trained backbones can already be competitive in repeat-order recommendation when paired with lightweight collaborative retrieval. Third, it establishes a reproducible evaluation setting for hybrid LLM recommenders in food delivery.
