World's Best Scientists 2026 revealed!
Award Badge
Computer Science
Singapore
2026

D-Index & Metrics

Computer Science

D-Index
123
Citations
71014
World Ranking
127
National Ranking
4

Research.com Recognitions

  • 2026 - Research.com Computer Science in Singapore Leader Award
  • 2025 - Research.com Computer Science in Singapore Leader Award
  • 2023 - Research.com Computer Science in Singapore Leader Award
  • 2022 - Research.com Computer Science in Singapore Leader Award

Overview

Tat-Seng Chua is affiliated with the National University of Singapore in Singapore. Their work primarily spans across the field of Computer Science, with a significant focus on several subfields including Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Management Science and Operations Research, and Computer Networks and Communications.

Their research contributions cover a broad range of topics within these areas. Main topics include:

  • Topic Modeling
  • Multimodal Machine Learning Applications
  • Recommender Systems and Techniques
  • Natural Language Processing Techniques
  • Advanced Graph Neural Networks
  • Domain Adaptation and Few-Shot Learning
  • Advanced Image and Video Retrieval Techniques

Tat-Seng Chua has published extensively, with notable recent papers including:

  • MGAT: Multimodal Graph Attention Network for Recommendation, 2020, Information Processing & Management
  • Self-Supervised Learning for Multimedia Recommendation, 2022, IEEE Transactions on Multimedia
  • Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering, 2021, arXiv (Cornell University)
  • Affective Image Content Analysis: Two Decades Review and New Perspectives, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Causal Attention for Interpretable and Generalizable Graph Classification, 2022, Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

In terms of publication venues, their work has appeared frequently in:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • ACM Transactions on Information Systems
  • IEEE Transactions on Multimedia
  • IEEE Transactions on Pattern Analysis and Machine Intelligence

Collaboration is notable in their career, with frequent co-authors including:

  • Fuli Feng
  • Xiang Wang
  • Xiangnan He
  • Wenqiang Lei
  • Yunshan Ma

Through this broad and multidisciplinary approach, Tat-Seng Chua's research has contributed to areas such as multimodal data analysis, recommendation systems, and graph-based learning methods, applying advanced techniques in artificial intelligence and machine learning.

Best Publications

  • Neural Collaborative Filtering

    Xiangnan He;Lizi Liao;Hanwang Zhang;Liqiang Nie

  • Neural Graph Collaborative Filtering

    Xiang Wang;Xiangnan He;Meng Wang;Fuli Feng

  • NUS-WIDE: a real-world web image database from National University of Singapore

    Tat-Seng Chua;Jinhui Tang;Richang Hong;Haojie Li

  • SCA-CNN: Spatial and Channel-Wise Attention in Convolutional Networks for Image Captioning

    Long Chen;Hanwang Zhang;Jun Xiao;Liqiang Nie

  • KGAT: Knowledge Graph Attention Network for Recommendation

    Xiang Wang;Xiangnan He;Yixin Cao;Meng Liu

  • Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

    Han Hu;Yonggang Wen;Tat-Seng Chua;Xuelong Li

  • Neural Factorization Machines for Sparse Predictive Analytics

    Xiangnan He;Tat-Seng Chua

  • Meta-Transfer Learning for Few-Shot Learning

    Qianru Sun;Yaoyao Liu;Tat-Seng Chua;Bernt Schiele

  • Fast Matrix Factorization for Online Recommendation with Implicit Feedback

    Xiangnan He;Hanwang Zhang;Min-Yen Kan;Tat-Seng Chua

  • Disentangled Graph Collaborative Filtering

    Xiang Wang;Hongye Jin;An Zhang;Xiangnan He

  • Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks

    Jun Xiao;Hao Ye;Xiangnan He;Hanwang Zhang

  • Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

    Jingyuan Chen;Hanwang Zhang;Xiangnan He;Liqiang Nie

  • Explainable Reasoning over Knowledge Graphs for Recommendation

    Xiang Wang;Dingxian Wang;Canran Xu;Xiangnan He

  • Temporal Relational Ranking for Stock Prediction

    Fuli Feng;Xiangnan He;Xiang Wang;Cheng Luo

  • Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences

    Yixin Cao;Xiang Wang;Xiangnan He;Zikun Hu

  • Visual Translation Embedding Network for Visual Relation Detection

    Hanwang Zhang;Zawlin Kyaw;Shih-Fu Chang;Tat-Seng Chua

  • MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video

    Yinwei Wei;Xiang Wang;Liqiang Nie;Xiangnan He

  • NAIS: Neural Attentive Item Similarity Model for Recommendation

    Xiangnan He;Zhankui He;Jingkuan Song;Zhenguang Liu

  • Hierarchical spatio-temporal context modeling for action recognition

    Ju Sun;Xiao Wu;Shuicheng Yan;Loong-Fah Cheong

  • Neural Sparse Voxel Fields

    Lingjie Liu;Jiatao Gu;Kyaw Zaw Lin;Tat-Seng Chua

  • Topical word embeddings

    Yang Liu;Zhiyuan Liu;Tat-Seng Chua;Maosong Sun

  • Tour the world: Building a web-scale landmark recognition engine

    Yan-Tao Zheng;Ming Zhao;Yang Song;Hartwig Adam

Frequent Co-Authors

Xiangnan He
Xiangnan He University of Science and Technology of China
Meng Wang
Meng Wang Hefei University of Technology
Liqiang Nie
Liqiang Nie Shandong University
Hanwang Zhang
Hanwang Zhang Nanyang Technological University
Richang Hong
Richang Hong Hefei University of Technology
Zheng-Jun Zha
Zheng-Jun Zha University of Science and Technology of China
Jinhui Tang
Jinhui Tang Nanjing University of Science and Technology
Shuicheng Yan
Shuicheng Yan National University of Singapore
Min-Yen Kan
Min-Yen Kan National University of Singapore
Fuli Feng
Fuli Feng University of Science and Technology of China

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring computer science in the USA often opens doors to a variety of related fields and flexible learning options. For students interested in completing their education quickly, a fast track computer science degree is a popular choice. These accelerated programs allow motivated students to enter the workforce faster without compromising on course quality.

Many learners are also choosing interdisciplinary approaches, such as combining computer science knowledge with environmental or engineering studies. For instance, those interested in sustainability and technology-powered solutions may find lucrative opportunities in high-paying jobs with environmental science degree backgrounds. These roles are increasingly in demand across various sectors.

Additionally, online education makes it more convenient and affordable to specialize. Options include an online environmental engineering degree or an online degree for mechanical engineering. These programs provide flexibility, making them ideal for working professionals or those needing to balance studies with personal commitments.

Best Scientists Citing Tat-Seng Chua

Trending Scientists

Recently Published Articles