Qiang Liu

Qiang Liu

Associate Professor

Chinese Academy of Sciences

Biography

I’m an Associate Professor at Institute of Automation, Chinese Academy of Sciences (CASIA). Currently, I mainly focus on data mining, recommender systems, text mining, knowledge graph and graph representation learning. Before that, I received my PhD degree from CASIA, where I mainly focused on recommender systems and text mining.

Interests

  • Data Mining
  • Recommender Systems
  • Text Mining
  • Knowledge Graph
  • Graph Representation Learning

Education

  • PhD in Artificial Intelligence, 2013-2018

    Institution of Automation, Chinese Academy of Sciences (CASIA)

  • BSc in Electronic Science, 2009-2013

    Yanshan University

Experience

 
 
 
 
 

Associate Professor

Institute of Automation, Chinese Academy of Sciences (CASIA)

Jul 2022 – Present Beijing, China
 
 
 
 
 

Assistant Professor

Institute of Automation, Chinese Academy of Sciences (CASIA)

Mar 2021 – Jun 2022 Beijing, China
 
 
 
 
 

Postdoctoral Researcher

Tsinghua University

Nov 2018 – Mar 2021 Beijing, China
 
 
 
 
 

R&D Director

RealAI

Jul 2018 – Sep 2020 Beijing, China
 
 
 
 
 

Research Intern

Microsoft Research Asia (MSRA)

May 2016 – Oct 2016 Beijing, China

Publications


    Selected Publications (# Corresponding Author):


  • GraphDIVE: Graph Classifcation by Mixture of Diverse Experts [paper]

    Fenyu Hu, Liping Wang, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    International Joint Conference on Artificial Intelligence (IJCAI), 2022


  • Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention [paper] [code]

    Junfei Wu, Qiang Liu, Weizhi Xu and Shu Wu

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2022


  • Evidence-aware Fake News Detection with Graph Neural Networks [paper] [arXiv] [code]

    Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu and Liang Wang

    The Web Conference (WWW), 2022


  • RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring [paper] [arXiv]

    Qiang Liu, Yingtao Luo, Shu Wu, Zhen Zhang, Xiangnan Yue, Hong Jin and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2022


  • An Empirical Study of Graph Contrastive Learning [paper] [arXiv] [code]

    Yanqiao Zhu, Yichen Xu, Qiang Liu and Shu Wu

    Advances in Neural Information Processing Systems (NeurIPS), 2021


  • Mining Cross Features for Financial Credit Risk Assessment [paper] [arXiv]

    Qiang Liu, Zhaocheng Liu, Haoli Zhang, Yuntian Chen and Jun Zhu

    ACM International Conference on Information and Knowledge Management (CIKM), 2021


  • Deep Active Learning for Text Classification with Diverse Interpretations [paper] [arXiv]

    Qiang Liu, Yanqiao Zhu, Zhaocheng Liu, Yufeng Zhang and Shu Wu

    ACM International Conference on Information and Knowledge Management (CIKM), 2021


  • Mining Latent Structures for Multimedia Recommendation [paper] [arXiv] [code]

    Jinghao Zhang, Yanqiao Zhu, Qiang Liu#, Shu Wu, Shuhui Wang and Liang Wang

    ACM International Conference on Multimedia (MM), 2021


  • STAN: Spatio-Temporal Attention Network for Next Point-of-Interest Recommendation [paper] [arXiv] [code]

    Yingtao Luo, Qiang Liu# and Zhaocheng Liu

    The Web Conference (WWW), 2021


  • Graph Contrastive Learning with Adaptive Augmentation [paper] [arXiv] [code]

    Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu and Liang Wang

    The Web Conference (WWW), 2021


  • MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation [paper]

    Qiang Cui, Shu Wu, Qiang Liu, Wen Zhong and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2020


  • Towards Accurate and Interpretable Sequential Prediction: A CNN & Attention-based Feature Extractor [paper] [code]

    Jingyi Wang, Qiang Liu, Zhaocheng Liu and Shu Wu

    ACM International Conference on Information and Knowledge Management (CIKM), 2019


  • Attention-based Convolutional Approach for Misinformation Identification from Massive and Noisy Microblog Posts [paper]

    Feng Yu, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    Computers & Security, 2019


  • Mining Significant Microblogs for Misinformation Identification: An Attention-based Approach [paper] [arXiv]

    Qiang Liu, Feng Yu, Shu Wu and Liang Wang

    ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2018


  • A Convolutional Approach for Misinformation Identification [paper]

    Feng Yu, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    International Joint Conference on Artificial Intelligence (IJCAI), 2017


  • DeepStyle: Learning User Preferences for Visual Recommendation [paper] [arXiv]

    Qiang Liu, Shu Wu and Liang Wang

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2017


  • Multi-behavioral Sequential Prediction with Recurrent Log-bilinear Model [paper] [arXiv]

    Qiang Liu, Shu Wu and Liang Wang

    IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE), 2017


  • Context-aware Sequential Recommendation [paper] [arXiv]

    Qiang Liu, Shu Wu, Diyi Wang, Zhaokang Li and Liang Wang

    IEEE International Conference on Data Mining (ICDM), 2016


  • A Dynamic Recurrent Model for Next Basket Recommendation [paper]

    Feng Yu, Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2016


  • Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts [paper]

    Qiang Liu, Shu Wu, Liang Wang and Tieniu Tan

    AAAI Conference on Artificial Intelligence (AAAI), 2016


  • A Convolutional Click Prediction Model [paper]

    Qiang Liu, Feng Yu, Shu Wu and Liang Wang

    ACM International Conference on Information and Knowledge Management (CIKM), 2015


  • Collaborative Prediction for Multi-entity Interaction with Hierarchical Representation [paper]

    Qiang Liu, Shu Wu and Liang Wang

    ACM International Conference on Information and Knowledge Management (CIKM), 2015


  • COT: Contextual Operating Tensor for Context-aware Recommender Systems [paper]

    Qiang Liu, Shu Wu and Liang Wang

    AAAI Conference on Artificial Intelligence (AAAI), 2015