SkyDistill: Navigating Fuzzy Flight Search Ranking via Precise Intent Distillation

Abstract

Fuzzy flight search is a vital traffic entry for large-scale platforms like Fliggy, serving over 200k daily active users. Unlike traditional fixed-itinerary searches, fuzzy search involves highly ambiguous intentions and flexible constraints, leading to low conversion rates (1.6% UV-CVR) due to sparse intent signals and complex user trade-offs. Standard ranking models struggle with the domain discrepancy between precise and fuzzy queries, often resulting in a misalignment between offline metrics and online performance. To address these challenges, we propose the Multi-level Cross-scenario Knowledge Distillation (MCKD) framework. MCKD transfers ''dark knowledge'' from a high-capacity Teacher model (trained on precise search data) to a lightweight Student (fuzzy search) model. Our framework introduces three core innovations: 1. Feature-level Hint Learning to align latent semantic representations across heterogeneous feature spaces; 2. Uncertainty-aware Distillation to adaptively weight knowledge transfer based on teacher confidence, mitigating noise propagation; 3. Pairwise Ranking Distillation to explicitly preserve the ranking manifold and relative preference orders. Extensive industrial evaluations and online A/B tests demonstrate that MCKD significantly outperforms state-of-the-art baselines and successfully translates offline gains into substantial online conversion improvements, offering a scalable solution for intent-vague retrieval tasks.