{"ID":2880106,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14420","arxiv_id":"2508.14420","title":"You Only Evaluate Once: A Tree-based Rerank Method at Meituan","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.","short_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...","url_abs":"https://arxiv.org/abs/2508.14420","url_pdf":"https://arxiv.org/pdf/2508.14420v1","authors":"[\"Shuli Wang\",\"Yinqiu Huang\",\"Changhao Li\",\"Yuan Zhou\",\"Yonggang Liu\",\"Yongqiang Zhang\",\"Yinhua Zhu\",\"Haitao Wang\",\"Xingxing Wang\"]","published":"2025-08-20T04:36:25Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[]","has_code":false}