Ruiming Tang

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 8 papers; trusts 31 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 144 citations into it; #21,150 of 35,345 by trust here, #8,283 by citations
• Information Systems — 92 citations into it; #3,882 of 8,771 by trust here, #948 by citations
• Computer Vision and Pattern Recognition — 64 citations into it; #13,243 of 21,575 by trust here, #6,737 by citations
• Recommender Systems — 25 citations into it; #157 of 233 by trust here, #79 by citations
• Model Architectures — 16 citations into it
• Graph Learning — 9 citations into it
• Information Retrieval & Search — 6 citations into it
• Statistical and Nonlinear Physics — 6 citations into it

Whom Ruiming Tang trusts overall

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

1. Steffen Rendle
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Taher H. Haveliwala
5. Alex Krizhevsky
6. Diederik P. Kingma
7. Kaiming He
8. Aäron van den Oord
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Ruiming Tang overall

1. Chuhan Wu
2. Fangzhao Wu
3. Le Wu
4. Fajie Yuan
5. Bin Liu
6. Minghong Fang
7. Mingxiao An
8. Aminu Da’u
9. Xiangnan He
10. Xing Xie

Papers from Ruiming Tang's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017) — 3,129 citations
• AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Prediction (2020) — 206 citations
• Mining target users for online marketing based on App Store data (2015) — 10 citations
• How Can Recommender Systems Benefit from Large Language Models: A Survey (2023) — 404 citations
• Neural Re-ranking in Multi-stage Recommender Systems: A Review (2022)
• AutoGroup: Automatic Feature Grouping for Modelling Explicit High-Order Feature Interactions in CTR Prediction (2020) — 60 citations
• GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems (2020) — 110 citations
• A Graph-Based Push Service Platform (2016) — 6 citations