@inproceedings{jain-etal-2026-trendpulse,
    title = "{T}rend{P}ulse: A Simple yet Efficient Framework for Capturing Viral {E}-Commerce Spikes via {LLM}-Driven Contextualization",
    author = "Jain, Arin  and
      Gupta, Devashish  and
      Singhal, Bhavuk  and
      Jindal, Divay  and
      Rongata, Vinit  and
      Yadav, Ravindra Kumar",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.64/",
    pages = "927--941",
    ISBN = "979-8-89176-394-4",
    abstract = "Anticipating and capturing transient demand spikes is a critical challenge for e-commerce platforms, as reactive discovery mechanisms often fail to surface relevant products during rapid cultural or seasonal shifts. We propose \textbf{TrendPulse}, a three-stage framework that identifies regional search momentum, leverages Large Language Model (LLM) to transform spikes into semantic trends, and employs a cross-attention mechanism to provide personalized catalog recommendations. Our comprehensive ablation experiments and evaluations validate the impact of each architectural component, showing consistent improvements across multiple critical business metrics. TrendPulse{'}s effectiveness is further validated through online A/B experiments, where it drives measurable gains in both business metrics and overall user experience. Finally, we outlined the deployment strategy in detail, providing a reproducible blueprint that can be readily applied to similar industry-scale applications."
}