@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://aclanthology.org/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."
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<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 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.</abstract>
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%0 Conference Proceedings
%T TrendPulse: A Simple yet Efficient Framework for Capturing Viral E-Commerce Spikes via LLM-Driven Contextualization
%A Jain, Arin
%A Gupta, Devashish
%A Singhal, Bhavuk
%A Jindal, Divay
%A Rongata, Vinit
%A Yadav, Ravindra Kumar
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F jain-etal-2026-trendpulse
%X 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 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.
%U https://aclanthology.org/2026.acl-industry.64/
%P 927-941
Markdown (Informal)
[TrendPulse: A Simple yet Efficient Framework for Capturing Viral E-Commerce Spikes via LLM-Driven Contextualization](https://aclanthology.org/2026.acl-industry.64/) (Jain et al., ACL 2026)
ACL