DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou
Qinyao Li, Xiaoyang Zheng, Qihang Zhao, Ke Xu, Zhongbo Sun, Chao Wang, Chenyi Lei, Han Li, Wenwu Ou

TL;DR
DiffusionGS introduces a generative, diffusion-based model that aligns user queries with behavioral data to improve personalized search relevance in short-video platforms.
Contribution
The paper presents a novel query-conditioned diffusion approach and the User-aware Denoising Layer for better interest extraction from noisy historical behaviors.
Findings
Outperforms state-of-the-art methods in offline experiments
Achieves significant online engagement improvements
Effectively captures dynamic user interest shifts
Abstract
Personalized search ranking systems are critical for driving engagement and revenue in modern e-commerce and short-video platforms. While existing methods excel at estimating users' broad interests based on the filtered historical behaviors, they typically under-exploit explicit alignment between a user's real-time intent (represented by the user query) and their past actions. In this paper, we propose DiffusionGS, a novel and scalable approach powered by generative models. Our key insight is that user queries can serve as explicit intent anchors to facilitate the extraction of users' immediate interests from long-term, noisy historical behaviors. Specifically, we formulate interest extraction as a conditional denoising task, where the user's query guides a conditional diffusion process to produce a robust, user intent-aware representation from their behavioral sequence. We propose the…
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