DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou

• URL: http://arxiv.org/abs/2508.17754v1
• Date: Mon, 25 Aug 2025 07:46:51 GMT
• Title: DiffusionGS: Generative Search with Query Conditioned Diffusion in Kuaishou
• Authors: Qinyao Li, Xiaoyang Zheng, Qihang Zhao, Ke Xu, Zhongbo Sun, Chao Wang, Chenyi Lei, Han Li, Wenwu Ou,
• Abstract summary: We propose DiffusionGS, a novel and scalable approach powered by generative models.<n>We formulate interest extraction as a conditional denoising task, where the user's query guides a conditional diffusion process.<n>We propose the User-aware Denoising Layer (UDL) to incorporate user-specific profiles into the optimization of attention distribution on the user's past actions.
• Score: 20.440076123934684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 User-aware Denoising Layer (UDL) to incorporate user-specific profiles into the optimization of attention distribution on the user's past actions. By reframing queries as intent priors and leveraging diffusion-based denoising, our method provides a powerful mechanism for capturing dynamic user interest shifts. Extensive offline and online experiments demonstrate the superiority of DiffusionGS over state-of-the-art methods.

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