Unbiased Multimodal Reranking for Long-Tail Short-Video Search

TL;DR

This paper introduces an LLM-driven multimodal reranking framework that improves long-tail short-video search quality by estimating user experience without relying on user behavior data, enhancing ranking fairness and relevance.

Contribution

The paper presents a novel two-stage training method leveraging multimodal evidence and preference optimization, enabling effective reranking without real user interaction data.

Findings

Achieves consistent offline improvements in AUC, NDCG@K, and human preferences.

Online A/B testing shows significant gains in user experience and consumption metrics.

Effectively promotes high-quality, underexposed videos in long-tail search scenarios.

Abstract

Kuaishou serving hundreds of millions of searches daily, the quality of short-video search is paramount. However, it suffers from a severe Matthew effect on long-tail queries: sparse user behavior data causes models to amplify low-quality content such as clickbait and shallow content. The recent advancements in Large Language Models (LLMs) offer a new paradigm, as their inherent world knowledge provides a powerful mechanism to assess content quality, agnostic to sparse user interactions. To this end, we propose a LLM-driven multimodal reranking framework, which estimates user experience without real user behavior. The approach involves a two-stage training process: the first stage uses multimodal evidence to construct high-quality annotations for supervised fine-tuning, while the second stage incorporates pairwise preference optimization to help the model learn partial orderings among candidates. At inference time, the resulting experience scores are used to promote high-quality but underexposed videos in reranking, and further guide page-level optimization through reinforcement learning. Experiments show that the proposed method achieves consistent improvements over strong baselines in offline metrics including AUC, NDCG@K, and human preference judgement. An online A/B test covering 15\% of traffic further demonstrates gains in both user experience and consumption metrics, confirming the practical value of the approach in long-tail video search scenarios.