Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction

• URL: http://arxiv.org/abs/2509.19876v1
• Date: Wed, 24 Sep 2025 08:28:33 GMT
• Title: Adaptive User Interest Modeling via Conditioned Denoising Diffusion For Click-Through Rate Prediction
• Authors: Qihang Zhao, Xiaoyang Zheng, Ben Chen, Zhongbo Sun, Chenyi Lei,
• Abstract summary: User behavior sequences in search systems resemble "interest fossils", capturing genuine intent yet eroded by exposure bias, category drift, and contextual noise.<n>We propose the Contextual Diffusion (CDP) to solve this problem.<n> CDP generates pure, context-aware interest representations that dynamically evolve with scenarios.
• Score: 13.938884910748584
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: User behavior sequences in search systems resemble "interest fossils", capturing genuine intent yet eroded by exposure bias, category drift, and contextual noise. Current methods predominantly follow an "identify-aggregate" paradigm, assuming sequences immutably reflect user preferences while overlooking the organic entanglement of noise and genuine interest. Moreover, they output static, context-agnostic representations, failing to adapt to dynamic intent shifts under varying Query-User-Item-Context conditions. To resolve this dual challenge, we propose the Contextual Diffusion Purifier (CDP). By treating category-filtered behaviors as "contaminated observations", CDP employs a forward noising and conditional reverse denoising process guided by cross-interaction features (Query x User x Item x Context), controllably generating pure, context-aware interest representations that dynamically evolve with scenarios. Extensive offline/online experiments demonstrate the superiority of CDP over state-of-the-art methods.

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