@inproceedings{ni-etal-2025-hierdiffuse,
    title = "{H}ier{D}iffuse: Progressive Diffusion for Robust Interest Fusion in {CTR} Prediction",
    author = "Ni, Ziheng  and
      Liu, Congcong  and
      Chen, Yuying  and
      Fang, Zhiwei  and
      Peng, Changping  and
      Lin, Zhangang  and
      Law, Ching  and
      Shao, Jingping",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.92/",
    pages = "1341--1348",
    ISBN = "979-8-89176-333-3",
    abstract = "Modern recommendation systems grapple with reconciling users' enduring preferences with transient interests, particularly in click-through rate (CTR) prediction. Existing approaches inadequately fuse long-term behavioral profiles (e.g., aggregated purchase trends) and short-term interaction sequences (e.g., real-time clicks), suffering from representational misalignment and noise in transient signals. We propose HierDiffuse, a unified framework that redefines interest fusion as a hierarchical denoising process through diffusion models. Our approach addresses these challenges via three innovations: (1) A cross-scale diffusion mechanism aligns long- and short-term representations by iteratively refining long-term interests using short-term contextual guidance; (2) A Semantic Guidance Disentanglement (SGD) mechanism explicitly decouples core interests from noise in short-term signals;(3) Trajectory Convergence Constraint (TCC) is proposed to accelerate diffusion model reasoning without reducing generation quality to meet the constraints of high QPS (Queries Per Second) and low latency for online deployment of recommendation or advertising systems.HierDiffuse eliminates ad-hoc fusion operators, dynamically integrates multi-scale interests, and enhances robustness to spurious interactions as well as improves inference speed. Extensive experiments on real-world datasets demonstrate state-of-the-art performance, with significant improvements in CTR prediction accuracy and robustness to noisy interactions. Our work establishes diffusion models as a principled paradigm for adaptive interest fusion in recommendation systems."
}