Briti Gangopadhyay


Fixing paper assignments

  1. Please select all papers that belong to the same person.
  2. Indicate below which author they should be assigned to.
Provide a valid ORCID iD here. This will be used to match future papers to this author.
Provide the name of the school or the university where the author has received or will receive their highest degree (e.g., Ph.D. institution for researchers, or current affiliation for students). This will be used to form the new author page ID, if needed.

TODO: "submit" and "cancel" buttons here


2025

pdf bib
OKG: On-the-Fly Keyword Generation in Sponsored Search Advertising
Zhao Wang | Briti Gangopadhyay | Mengjie Zhao | Shingo Takamatsu
Proceedings of the 31st International Conference on Computational Linguistics: Industry Track

Current keyword decision-making in sponsored search advertising relies on large static datasets, limiting automatic keyword setup and failing to adapt to real-time KPI metrics and product updates essential for effective advertising. In this paper, we propose On-the-fly Keyword Generation (OKG), an LLM agent-based method that dynamically monitors KPI changes and adapts keyword generation in real-time, realizing the strategy recommended by advertising platforms. Additionally, we introduce the first publicly accessible dataset containing real keyword data with its KPIs across diverse domains, providing a valuable resource for future research. Experimental results and ablation studies demonstrate the effectiveness of OKG, showing significant improvements across various metrics and emphasizing the importance of each component. We believe OKG not only pioneers the use of LLM agents in this research field but also offers practical value for thousands of advertisers to automate keyword generation in real-world applications.