Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous queries under strict latency constraints. Existing LLM-based methods have improved rewriting by
WeFair improves personalized query rewriting via fidelity-aware human feedback stages—demonstration, preference, and verification—to reduce intent drift and boost search effectiveness in short-video platforms.
Short-video search needs compact query rewriters that recover users' personalized intent from ambiguous queries under strict latency constraints. Existing LLM-based methods have improved rewriting by using synthetic reasoning and subsequent consumption-behavioral feedback. However, overemphasizing subsequent consumption can favor engaging content over the user's immediate need, causing intent drift even when rewritten queries are grounded in behavioral history. We propose WeFair, Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in WeChat short-video search. WeFair uses human feedback according to how directly it reflects personalized intent, assigning feedback…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
WeFair: Fidelity-Aware Alignment from Human Feedback for Personalized Query Rewriting in Short-Video Search was first seen on Hugging Face · Daily papers on Nov 2, 2026. Since then it has appeared in 9 checks on 2 different days. Its best position was #7 of 30, reached on Nov 2, 19:28 UTC. It dropped off the list after Nov 3, 07:28 UTC.
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It was first seen on Nov 2, 2026 and has been on the list on 2 days (9 checks) since.