Awesome AI Agents
📄
Papers
🧭
Topics
🔥
Trending
🗺️
Map
🏆
Leaderboards
🎓
Learn
🤖
Ask AI
⋯
More
👥
Authors
📚
Reading Packs
📊
Datasets
🛠️
Tools
📰
News
📝
Blogs
✉️
Newsletter
🎯
Research Radar
🔖
Saved
+ Add Paper
☾
☀
← authors
·
overview
Loading author…
🤖
Ask AI
Zhenhua Dong — most-cited papers & profile · AI Agents
← authors
·
overview
Zhenhua Dong
16
papers ·
46
citations ·
33
h-index
Jilin University · Huawei Technologies (China) · First Hospital of Jilin University
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
2025 · 31 citations
RecUserSim: A Realistic and Diverse User Simulator for Evaluating Conversational Recommender Systems
2025 · 9 citations
Evaluating Conversational Recommender Systems via Large Language Models: A User-Centric Framework
2025 · 3 citations
Evaluating Conversational Recommender Systems via Large Language Models: A User-Centric Framework
2025 · 3 citations
AdaFlash: Adaptive Speculative Decoding via On-Policy Distilled Diffusion Drafters
2026
FAIR: Focused Attention Is All You Need for Generative Recommendation
2025
Act-With-Think: Chunk Auto-Regressive Modeling for Generative Recommendation
2025
Regret-aware Re-ranking for Guaranteeing Two-sided Fairness and Accuracy in Recommender Systems
2025
EAGER-LLM: Enhancing Large Language Models as Recommenders through Exogenous Behavior-Semantic Integration
2025
A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems
2022
IntTower: the Next Generation of Two-Tower Model for Pre-Ranking System
2022
Bounding System-Induced Biases in Recommender Systems with A Randomized Dataset
2023
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
2023
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
2024
Bias and Unfairness in Information Retrieval Systems: New Challenges in the LLM Era
2024
Topics
cs.IR
cs.AI
cs.HC
RAG
Efficiency
Model Architecture
Training Techniques
Evaluation
In-Context Learning
Prompting