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Author

Huifeng Guo

Observed name: Guo, Huifeng
Observed institution: Huawei Technologies (China)
ORCID: 0000-0002-7393-8994
Observed ORCID: 0000-0002-7393-8994
OpenAlex ID: A5122688455
LLMTreeRec: Unleashing the Power of Large Language Models for Cold-Start Recommendations
2024 · Zhang, Wenlin, Wu, Chuhan, et al. · arXiv (Cornell University) · 20
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Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
2026 · Yingyi Zhang, Pengyue Jia, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 7
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Don't Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
2026 · Zhao, Ziyi, Gao, Chongming, et al. · arXiv (Cornell University) · 7
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SampleLLM: Optimizing Tabular Data Synthesis in Recommendations
2025 · Gao, Jingtong, Du, Zhaocheng, et al. · arXiv (Cornell University) · 7
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Exploring Recommender System Evaluation: A Multi-Modal User Agent Framework for A/B Testing
2026 · Zhang, Wenlin, Li, Xiangyang, et al. · arXiv (Cornell University) · 6
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Evoking User Memory: Personalizing LLM via Recollection-Familiarity Adaptive Retrieval
2026 · Zhang, Yingyi, Li, Junyi, et al. · arXiv (Cornell University) · 5
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Can Recommender Systems Teach Themselves? A Recursive Self-Improving Framework with Fidelity Control
2026 · Zhang, Luankang, Wang, Hao, et al. · arXiv (Cornell University) · 5
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Exploring Recommender System Evaluation: A Multi-Modal LLM Agent Framework for A/B Testing
2026 · Wenlin Zhang, Xiangyang Li, et al. · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · 3
Personalized Deep Research: A User-Centric Framework, Dataset, and Hybrid Evaluation for Knowledge Discovery
2026 · Xiaopeng Li, Wenlin Zhang, et al. · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · 2
P-Law: Predicting Quantitative Scaling Law with Entropy Guidance in Large Recommendation Models
2025 · Tingjia Shen, Hao Wang, et al. · neural information processing systems · 2
Towards Fast Domain Adaptation and Fine-Grained User Simulation for Evaluating Conversational Recommender Systems
2026 · Li, Yuanzi, Dai, Quanyu, et al. · arXiv (Cornell University) · 1
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From Raw IDs to Semantic Planning: How Recommender Systems Utilize Information at Scale
2026 · Jin, Changhong, Yang, Shiqiu, et al. · arXiv (Cornell University)
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Don’t Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
2026 · Ziyi Zhao, Chongming Gao, et al. · Proceedings of the AAAI Conference on Artificial Intelligence
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Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
2026 · Association for Artificial Intelligence 2026, Gan, Weinan, et al. · Underline Science Inc.
114 - Personalize Before Retrieve: LLM-based Personalized Query Expansion for User-Centric Retrieval
2026 · Association for Artificial Intelligence 2026, Gan, Weinan, et al. · Underline Science Inc.
170 - Don’t Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
2026 · Association for Artificial Intelligence 2026, Feng, Fuli, et al. · Underline Science Inc.
Don’t Start Over: A Cost-Effective Framework for Migrating Personalized Prompts Between LLMs
2026 · Association for Artificial Intelligence 2026, Feng, Fuli, et al. · Underline Science Inc.
Works count: 17
Citation count: 65
H-index: 5
I10-index: 1
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