Large Language Models Empowered Personalized Web Agents
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.
Authors
- Fengbin Zhu (ORCID: https://orcid.org/0000-0001-6776-2040)
- Hongru Cai (ORCID: https://orcid.org/0009-0007-9857-6639)
- Tat‐Seng Chua (ORCID: https://orcid.org/0000-0001-6097-7807)
- Wenjie Wang (ORCID: https://orcid.org/0000-0002-5199-1428)
- Wenjie Li (ORCID: https://orcid.org/0000-0002-7360-8864)
- Yongqi Li (ORCID: https://orcid.org/0000-0002-6932-4228)
- Xiaoyu Shen (ORCID: https://orcid.org/0000-0002-0217-2469)
Institutions
- University of Science and Technology of China (CN)
- Hong Kong Polytechnic University (HK)
- National University of Singapore (SG)
- Eastern Institute of Technology, Ningbo
Publication Details
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1145/3696410.3714842
- Citations
- 3
- Primary Topic
- Topic Modeling
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- Hong Kong Polytechnic University