Consistency-Aware Online Multi-Objective Alignment for Related Search Query Generation

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 Consistent Multi-Objective Aligned Query 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 β-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 stateof-the-art methods. Our approach enables highquality query generation while sustaining user engagement and platform ecosystem health.