Chen Gao

Artificial Intelligence · Annual International ACM SIGIR Conference on Research and Development in Information Retrieval · 2019–2023

A researcher in the Science corpus: 10 papers; trusts 107 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 297 citations into it; #33,323 of 35,345 by trust here, #16,166 by citations
• Information Systems — 171 citations into it; #8,309 of 8,771 by trust here, #3,315 by citations
• Computer Vision and Pattern Recognition — 120 citations into it
• Recommender Systems — 55 citations into it
• Management Science and Operations Research — 51 citations into it
• Graph Learning — 38 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Statistical and Nonlinear Physics — 23 citations into it

Whom Chen Gao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Xiangnan He
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Yehuda Koren
6. Steffen Rendle
7. Ruslan Salakhutdinov
8. Kaiming He
9. Xiang Wang
10. Alex Krizhevsky

Who trusts Chen Gao overall

1. Shiwen Wu
2. Yu Zheng

Papers from Chen Gao's perspective

Ordered by how strongly the researchers Chen Gao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• λOpt: Learn to Regularize Recommender Models in Finer Levels (2019) — 47 citations
• Sequential Recommendation with Graph Neural Networks (2021) — 482 citations
• S3: Social-network Simulation System with Large Language Model-Empowered Agents (2023) — 331 citations
• Large language models empowered agent-based modeling and simulation: a survey and perspectives (2023) — 471 citations
• Graph Neural Networks for Recommender System (2022) — 318 citations
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021) — 789 citations
• Disentangling User Interest and Conformity for Recommendation with Causal Embedding (2021) — 413 citations
• Multi-behavior Recommendation with Graph Convolutional Networks (2020) — 441 citations
• Cross-domain Recommendation Without Sharing User-relevant Data (2019) — 146 citations
• Membership Inference Attacks against Generative Models with Probabilistic Fluctuation