Large language models (LLMs) have achieved remarkable success across diverse applications, yet their generic training paradigm limits effectiveness in user-specific scenarios. LLM personalization aims to adapt large models to individual users or user groups by incorporating preferences, histories, and contextual signal...
Rui-Jie Wang, Qing-Kai Zeng, Xuefei Wang et al.· Proceedings of the 32nd ACM...· 0 citations
The unprecedented expansion of global metropolises has modernized many people's lives, yet it has also engendered significant challenges, such as air pollution, increased energy consumption, and traffic congestion. Tackling these deeply intertwined challenges was nearly impossible years ago given the complex, dynamic,...
Sijie Ruan, Yu Zheng, Yuxuan Liang et al.· Proceedings of the 32nd ACM...· 0 citations
The field of information retrieval has been rapidly transformed by AI technologies, especially large language model (LLM) agents with strong reasoning, planning, and conversational capabilities. These AI agents have improved how information is retrieved, processed, and personalized across search and recommendation syst...
Qingsong Wen, P. Mehrotra, Yongfeng Zhang et al.· Proceedings of the 32nd ACM...· 0 citations
The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)" workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex d...
Qing-Yun Sun, Zi-Wei Zhang, Xing-Cheng Fu et al.· Proceedings of the 32nd ACM...· 0 citations
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