You Only Evaluate Once: A Tree-based Rerank Method at Meituan
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916909442662400 |
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| author | Wang, Shuli Huang, Yinqiu Li, Changhao Zhou, Yuan Liu, Yonggang Zhang, Yongqiang Zhu, Yinhua Wang, Haitao Wang, Xingxing |
| author_facet | Wang, Shuli Huang, Yinqiu Li, Changhao Zhou, Yuan Liu, Yonggang Zhang, Yongqiang Zhu, Yinhua Wang, Haitao Wang, Xingxing |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_14420 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | You Only Evaluate Once: A Tree-based Rerank Method at Meituan Wang, Shuli Huang, Yinqiu Li, Changhao Zhou, Yuan Liu, Yonggang Zhang, Yongqiang Zhu, Yinhua Wang, Haitao Wang, Xingxing Information Retrieval 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. |
| title | You Only Evaluate Once: A Tree-based Rerank Method at Meituan |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.14420 |