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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

Published 2025 in International Conference on Information and Knowledge Management

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 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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