Researcher

Aixin Sun

Aixin Sun is an Associate Professor at Nanyang Technological University in Singapore who studies information retrieval, recommender systems and named entity recognition. After a B.A.Sc and a PhD at NTU in 2001 and 2004, he joined its faculty in 2005. A 2013 paper he coauthored with Gao Cong and others, on time-aware point-of-interest recommendation, received a SIGIR 2025 Test of Time Honorable Mention. Surveys of deep learning for recommender systems (2017) and for named entity recognition (2018) carry his name as coauthor, and a 2023 study with Yitong Ji, Jie Zhang and Chenliang Li examined data leakage in offline recommender evaluation. He is Associate Dean for Undergraduate Education in NTU's College of Computing and Data Science.

Timeline

  1. 2001–2004

    B.A.Sc and PhD at NTU

    He earns a B.A.Sc with first class honours in 2001 and a PhD in 2004, both from the School of Computer Science and Engineering at Nanyang Technological University (NTU) in Singapore.

  2. Since 2005

    Joining the NTU faculty

    He joins the NTU faculty in 2005, in what is now the College of Computing and Data Science. His research interests include information retrieval, text and web mining, social computing, multimedia and digital libraries.

  3. 2013

    Time-aware point-of-interest recommendation

    With Quan Yuan, Gao Cong, Zongyang Ma and Nadia Magnenat-Thalmann, he presents 'Time-aware point-of-interest recommendation' at SIGIR 2013. The paper models user behaviour and location data to predict where people want to go next.

  4. 2017

    Demo chair at CIKM 2017

    He serves as a Demo Track chair of CIKM 2017, together with Vincent S. Tseng of National Cheng Kung University and Chenliang Li of Wuhan University.

  5. 2017–2020

    Two deep learning surveys

    In 2017, he coauthors a survey of deep learning based recommender systems with Shuai Zhang, Lina Yao and Yi Tay, accepted by ACM Computing Surveys. In December 2018, he coauthors a survey of deep learning for named entity recognition with Jing Li, Jianglei Han and Chenliang Li, revised in March 2020.

  6. 2020–2024

    Data leakage in recommender evaluation

    In October 2020, he and coauthors Yitong Ji, Jie Zhang and Chenliang Li post a study of data leakage in offline evaluation of recommender systems, published in ACM Transactions on Information Systems in 2023. His perspectives paper 'Take a Fresh Look at Recommender Systems from an Evaluation Standpoint', accepted for SIGIR 2023, re-examines train/test data splits and the global timeline. In 2024, he publishes the single-author essay 'Beyond Collaborative Filtering: A Relook at Task Formulation in Recommender Systems' in the SIGWEB Newsletter.

  7. 2025

    Test of Time Honorable Mention

    The 2013 paper 'Time-aware point-of-interest recommendation' earns the ACM SIGIR 2025 Test of Time Honorable Mention Award. NTU announces the award in August 2025.

  8. As of October 2026

    Associate Dean at NTU

    He is an Associate Professor at NTU's College of Computing and Data Science and serves as its Associate Dean for Undergraduate Education.

Career

  1. Since 2005Nanyang Technological UniversityAssociate Professor

Education

  1. 2004Nanyang Technological UniversityPhD
  2. 2001Nanyang Technological UniversityB.A.Sc.

Sources and credits

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  1. Our People | Data Management and Analytics Lab (DMAL) | NTU Singapore ntu.edu.sg. Checked 21 October 2026.
  2. Our People | Research | NTU Singapore ntu.edu.sg. Checked 21 October 2026.
  3. CCDS Research Honoured with SIGIR 2025 Test of Time Award | College of Computing and Data Science | NTU Singapore ntu.edu.sg. Checked 21 October 2026.
  4. Test of Time Award Winners | SIGIR sigir.org. Checked 21 October 2026.
  5. CIKM 2017 cikm2017.org. Checked 21 October 2026.
  6. [1707.07435] Deep Learning based Recommender System: A Survey and New Perspectives arxiv.org. Checked 21 October 2026.
  7. [1812.09449] A Survey on Deep Learning for Named Entity Recognition arxiv.org. Checked 21 October 2026.
  8. [2010.11060v4] A Critical Study on Data Leakage in Recommender System Offline Evaluation arxiv.org. Checked 21 October 2026.
  9. [2210.04149] Take a Fresh Look at Recommender Systems from an Evaluation Standpoint arxiv.org. Checked 21 October 2026.
  10. [2404.13375] Beyond Collaborative Filtering: A Relook at Task Formulation in Recommender Systems arxiv.org. Checked 21 October 2026.
  11. Deanery | College of Computing and Data Science | NTU Singapore ntu.edu.sg. Checked 21 October 2026.