Large Language Models Empowered Personalized Web Agents
Open accessAbstract
Web agents have emerged as a promising direction to automate Web task completion based on user instructions, significantly enhancing user experience. Recently, Web agents have evolved from traditional agents to Large Language Models (LLMs)-based Web agents. Despite their success, existing LLM-based Web agents overlook the importance of personalized data (e.g., user profiles and historical Web behaviors) in assisting the understanding of users' personalized instructions and executing customized actions.
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Agents strive to adapt along two primary empirical dimensions: user-facing personalization and task-oriented specialization.
“particularly as agents strive to adapt along two primary empirical dimensions: user-facing personalization (Cai et al., 2025; Zhang et al., 2025o; Wu et al., 2025e) and task-oriented specialization (Ling et al., 2025; Zhang et al., 2025i).”
From A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents (2026) · cites this paper as Cai et al., 2025
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- Authors
- Cai, H., Li, Y., Wang, W., Zhu, F., Shen, X., Li, W., & Chua, T.-S.
- Affiliations
- National University of Singapore (SG)
- Hong Kong Polytechnic University (HK)
- University of Science and Technology of China (CN)
- Eastern Institute of Technology, Ningbo (CN)
- Venue
- Proceedings of the ACM on Web Conference 2025
- Publisher
- ACM
- Published
- 2025-04-22
- Pages
- 198-215
- OpenAlex
- W4409657290
- Cited by
- 15
- References
- 21
- Citations by year
- 2026: 11 · 2025: 4
- FWCI
- 18.00
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by-nc)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- PolyU Internal Grants (BDWP)
- Research Grants Council of Hong Kong (PolyU/15209724, PolyU/15207821, PolyU/15207122, PolyU/15213323)
- Topics
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- large language models, web agents, personalized agents, user profiles, personalized instruction, user understanding
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