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arXiv · 2508.14420

You Only Evaluate Once: A Tree-based Rerank Method at Meituan

Shuli Wang·Yinqiu Huang·Changhao Li·Yuan Zhou·Yonggang Liu·Yongqiang Zhang·Yinhua Zhu·Haitao Wang·Xingxing Wang
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Abstract

Reranking plays a crucial role in modern recommender systems by capturing the mutual influences within the list. Due to the inherent challenges of combinatorial search spaces, most methods adopt a two-stage search paradigm: a simple General Search Unit (GSU) efficiently reduces the candidate space, and an Exact Search Unit (ESU) effectively selects the optimal sequence. These methods essentially involve making trade-offs between effectiveness and efficiency, while suffering from a severe \textbf{inconsistency problem}, that is, the GSU often misses high-value lists from ESU. To address this problem, we propose YOLOR, a one-stage reranking method that removes the GSU while retaining only the ESU. Specifically, YOLOR includes: (1) a Tree-based Context Extraction Module (TCEM) that hierarchically aggregates multi-scale contextual features to achieve "list-level effectiveness", and (2) a Context Cache Module (CCM) that enables efficient feature reuse across candidate permutations to achieve "permutation-level efficiency". Extensive experiments across public and industry datasets validate YOLOR's performance, and we have successfully deployed YOLOR on the Meituan food delivery platform.

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Shuli Wang, Yinqiu Huang, Changhao Li, Yuan Zhou, Yonggang Liu, Yongqiang Zhang, Yinhua Zhu, Haitao Wang, Xingxing Wang. 2025-08-20. You Only Evaluate Once: A Tree-based Rerank Method at Meituan. https://doi.org/10.1145/3746252.3761539

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