Kaiqi Zhao

Kaiqi Zhao


Science Centre, Building 303, Level 4, Room 492
School of Computer Science
38 Princes Street, Auckland 1010
Email: kaiqi.zhao[at]auckland.ac.nz

I am currently a Lecturer with the School of Computer Science at the University of Auckland and a member of Machine Learning Group @ UoA. I have a broad research interests in machine learning and data mining. Specifically, my recent research focuses on spatio-temporal data mining, text mining and recommender systems. I am interested in building machine learning models to address practical problems such as geo-topic mining, GPS mobility modeling and user preference mining. 

Prospective graduate student: I am looking for self-motivated and hard-working Honors/Master/PhD students to do exciting research in spatio-temporal data mining, text mining and recommender system. Please email your CV if you are interested!


  • 2019 – now:  Lecturer, School of Computer Science, University of Auckland.
  • 2018 – 2019: Research Fellow, Singtel Cognitive and Aritificial Intelligence Lab for Enterprise, NTU.
  • 2013 – 2018: Ph.D. in Computer Science, School of Computer Science & Engineering, Nanyang Technological University. Supervisor: Dr. Gao CONG.
  • 2011 – 2012: Intern, Web Search & Mining Group, Microsoft Research Asia. Mentor: Dr. Haixun Wang.
  • 2010 – 2013: M.Eng. in Computer Engineering, Department of Computer Science, Shanghai Jiao Tong University. Supervisor: Dr. Kenny Q. Zhu.
  • 2007 – 2009: B.A. in Japanese Language, School of Foreign Languages, Huazhong University of Science and Technology.
  • 2005 – 2009: B.Eng. in Software Engineering, School of Software Engineering, Huazhong University of Science and Technology.


Spatio-temporal data mining

The prevalence of location positioning devices such as GPS have made huge amount of geo-tagged data available. My research on spatio-temporal data mining is to automatically uncover latent patterns from data associated with GPS coordinates over time. This research area links to various of applications including GPS trajectory mining, local event detection, location prediction and traffic prediction.

Text mining

Mining big text data is challenging because of its unstructured nature. In this research area, I focus on developing scalable models for understanding big text data. Specifically, we study the problems of efficient topic mining from tweets; sentiment analysis for online location-based reviews; named entities disambiguation via entity linking; and text summarization with knowledge graphs

Recommender systems

Recommender system is a common practice to address the information overload problem and is widely applied in many industries such as journalism, e-commerce system and location-based services. My approach is to use advanced machine learning techniques on interesting recommendation problems, such as location-based recommendations, session-based recommendation, long-short term user preference modeling.


  • Space Meets Time: Local Spacetime Neural Network For Traffic Flow Forecasting
    Song Yang, Jiamou Liu, Kaiqi Zhao
    ICDM, 2021

  • Node2LV: Squared Lorentzian Representations for Node Proximity
    Shanshan Feng, Lisi Chen, Kaiqi Zhao, Wei Wei, Fan Li, Shuo Shang
    ICDE, 2021

  • PGeoTopic: A Distributed Solution for Mining Geographical Topic Models
    Kaiqi Zhao, Gao Cong, Xiucheng Li
    TKDE, 2020

  • Online Anomalous Trajectory Detection with Deep Generative Sequence Modeling [pdf][codes]
    Yiding Liu, Kaiqi Zhao, Gao Cong, Zhifeng Bao
    ICDE, 2020

  • EdgeRec: Recommender System on Edge in Mobile Taobao
    Yu Gong, Ziwen Jiang, Yufei Feng, Binbin Hu, Kaiqi Zhao, Qingwen Liu, Wenwu Ou
    CIKM, 2020

  • A Novel Model for Imbalanced Data Classification
    Jian Yin, Chunjing Gan, Kaiqi Zhao, Xuan Lin, Zhe Quan, Zhi-Jie Wang
    AAAI, 2020

  • Knowledge-Enhanced Ensemble Learning for Word Embeddings [codes]
    Lanting Fang, Yong Luo, Kaiyu Feng, Kaiqi Zhao, Aiqun Hu
    WWW, 2019

  • Exploring Market Competition over Topics in Spatio-Temporal Document Collections
    Kaiqi Zhao,  Gao Cong, Jin-Yao Chin, Rong Wen.
    VLDB Journal, 2019

  • Efficient Similar Region Search with Deep Metric Learning [pdf]
    Yiding Liu, Kaiqi Zhao, Gao Cong.
    SIGKDD, 2018

  • ANR: Aspect-based Neural Recommender [pdf]
    Jin-Yao Chin, Kaiqi Zhao, Shafiq Joty, Gao Cong.
    CIKM, 2018

  • Deep Representation Learning for Trajectory Similarity Computation [pdf][codes]
    Xiucheng Li, Kaiqi Zhao, Gao Cong, Christian S. Jensen, Wei Wei.
    ICDE, 2018

  • Biclustering: An application of Dual Topic Models 
    Daniel Rugeles, Kaiqi Zhao, Gao Cong, Manoranjan Dash, Shonali  Krishnaswamy.
    SDM, 2017

  • Annotating Points of Interest with Geo-tagged Tweets [pdf]
    Kaiqi Zhao, Gao Cong, Aixin Sun.
    CIKM, 2016

  • Towards Personalized Maps: Mining User Preferences from Geo-textual Data (Demo paper) [pdf]
    Kaiqi Zhao, Yiding Liu, Quan Yuan, Lisi Chen, Zhida Chen, Gao Cong.
    VLDB, 2016

  • A System for Region Search and Exploration (Demo paper) [pdf]
    Kaiyu Feng, Kaiqi Zhao, Yiding Liu, Gao Cong.
    VLDB, 2016

  • Topic Exploration in Spatio-Temporal Document Collections 
    Kaiqi Zhao, Lisi Chen, Gao Cong.
    SIGMOD, 2016

  • Querying and mining geo-textual data for exploration: Challenges and opportunities
    Gao Cong, Kaiyu Feng, Kaiqi Zhao.
    ICDE Workshops, 2016

  • Representing Verbs as Argument Concepts [pdf]
    Yu Gong, Kaiqi Zhao, Kenny Q. Zhu.
    AAAI, 2016

  • Who, where, when and what: a nonparametric bayesian approach to context-aware recommendation and search for twitter users
    Quan Yuan, Gao Cong, Kaiqi Zhao, Zongyang Ma, Aixin Sun.
    TOIS, 2015

  • SAR: A Sentiment-Aspect-Region Model for User Preference Analysis in Geo-tagged Reviews [pdf]
    Kaiqi Zhao, Gao Cong, Quan Yuan, Kenny Q. Zhu.
    ICDE, 2015

  • Clustering Image Search Results by Entity Disambiguation
    Kaiqi Zhao, Zhiyuan Cai, Qingyu Sui, Enxun Wei, Kenny Q. Zhu.
    ECML/PKDD, 2014

  • Wikification via link co-occurrence
    Zhiyuan Cai, Kaiqi Zhao, Kenny Q. Zhu, Haixun Wang.
    CIKM, 2013

  • CISC: Clustered Image Search by Conceptualization (Demo paper)
    Kaiqi Zhao, Enxun Wei, Qingyu Sui, Kenny Q. Zhu, Eric Lo.
    EDBT, 2013


  • Algorithms for Massive Data (COMPSCI753), Instructor, 2020 S2, UoA
  • Data-mining and Machine Learning (COMPSCI760), Instructor, 2020 S2, UoA
  • Artificial Intelligence (COMPSCI367), Instructor, 2020 S1, UoA-SWU
  • Data-mining and Machine Learning (COMPSCI760), Instructor, 2019 S2, UoA
  • Machine Learning (COMPSCI361), Guest Lecturer, 2019 S2, UoA
  • Introduction to Database (CZ2007), Teaching Assistant, Fall 2016, NTU
  • Database System Principles (CZ4031), Teaching Assistant, Fall 2015, NTU
  • Windows Internals (CS490), Teaching Assistant, Fall 2010, SJTU

Professional Services

Program Committee Member:

  • ADMA 2017, CIKM 2017, WSDM 2021, PAKDD 2021, IJCAI 2021, KDD 2021
  • AAAI since 2020
  • SIGIR since 2020

Journal Reviewer:

  • TOIS, TKDE, TKDD, IEEE Trans. on Big Data, World Wide Web Journal.

External Reviewer:

  • 2018: KDD, SIGMOD, ICDE, WWW
  • 2016: WWW, AAAI, CIKM, ICDM
  • 2015: WWW, ACL, Trans. Big Data
  • Others: WWW 2013, EMNLP 2013, CIKM 2012, WWW 2011, ECML 2011