MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation
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 gl...
| Authors | Jiahao Tian, Zhenkai Wang |
|---|---|
| Primary category | cs.IR |
| Published | 28 July 2026 |
| Last updated at source | 28 July 2026 |
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