Leveraging Large Language Models (LLMs)-synthesized data to enable representation learning for cold-start entities has emerged as a promising solution for addressing cold-start recommendations. However, existing studies often over-rely on LLM-generated data while neglecting the detrimental noise it may introduce. This...
Tian-Hao Shi, Yang Zhang, Xiaoxue Zang et al.· ACM Transactions on Informat...· 0 citations
The workshop brings together researchers and practitioners from data mining, LLMs, NLP, NLP, IR, human-centered AI, and AI safety to position personalization as a central research direction for next-generation AI systems at KDD.
Xiaoyan Zhao, Yang Zhang, Mo-Xin Li et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes a model-agnostic framework to assess the accuracy of imputed labels and to correct imputations with large bias based on conformal prediction, and leverage conformal prediction to construct a valid prediction set for units with unobserved labels, and revise imputations that fall outside this set.
Chun-Yuan Zheng, Xiang Li, Hang Pan et al.· Proceedings of the 32nd ACM...· 0 citations
Large language models (LLMs) and agentic AI systems are rapidly moving into user-facing applications, yet most remain fundamentally generic, optimized for population-level objectives under the assumption that one model can serve all users. This assumption is increasingly misaligned with real-world deployment, where AI...
Xiaoyan Zhao, Yang Zhang, Moxin Li et al.· Proceedings of the 32nd ACM...· 0 citations
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