@inproceedings{bi-etal-2025-consistency,
    title = "Consistency-Aware Online Multi-Objective Alignment for Related Search Query Generation",
    author = "Bi, Shuxian  and
      Gao, Chongming  and
      Wang, Wenjie  and
      Mou, Yueqi  and
      Wang, Chenxu  and
      Biao, Tang  and
      Yan, Peng  and
      Feng, Fuli",
    editor = "Rehm, Georg  and
      Li, Yunyao",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.acl-industry.96/",
    pages = "1365--1377",
    ISBN = "979-8-89176-288-6",
    abstract = "Modern digital platforms rely on related search query recommendations to enhance engagement, yet existing methods fail to reconcile click-through rate (CTR) optimization with topic expansion. We propose **CMAQ**, a **C**onsistent **M**ulti-Objective **A**ligned **Q**uery generation framework that harmonizes these goals through three components: (1) reward modeling to quantify objectives, (2) style alignment for format compliance, and (3) consistency-aware optimization to coordinate joint improvements. CMAQ employs adaptive $\beta$-scaled DPO with geometric mean rewards, balancing CTR and expansion while mitigating objective conflicts. Extensive offline and online evaluations in a large-scale industrial setting demonstrate CMAQ{'}s superiority, achieving significant CTR gains (+2.3{\%}) and higher human-rated query quality compared to state-of-the-art methods. Our approach enables high-quality query generation while sustaining user engagement and platform ecosystem health."
}