Qiang Liu

Qiang Liu

Assistant Professor

Chinese Academy of Sciences

Biography

I’m an Assistant 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 was a Postdoctoral Researcher at Tsinghua University. I was also a R&D Director at RealAI, where I led the machine learning team to work on AutoML and Explainable AI, for delivering products on FinTech. 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

 
 
 
 
 

Assistant Professor

Institute of Automation, Chinese Academy of Sciences (CASIA)

Mar 2021 – Present 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


        (*: Equal Contribution, #: Corresponding Author)


        Preprints


  • Deep Graph Structure Learning for Robust Representations: A Survey [arXiv]

    Yanqiao Zhu, Weizhi Xu, Jinghao Zhang, Qiang Liu, Shu Wu and Liang Wang


    2021


  • An Empirical Study of Graph Contrastive Learning [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


  • Relation-aware Heterogeneous Graph for User Profiling [paper] [arXiv]

    Qilong Yan, Yufeng Zhang, Qiang Liu, Shu Wu and Liang Wang

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


  • Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction [paper] [arXiv] [code]

    Yichen Xu, Yanqiao Zhu, Feng Yu, Qiang Liu 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


  • Disentangled Item Representation for Recommender Systems [paper] [arXiv]

    Zeyu Cui, Feng Yu, Shu Wu, Qiang Liu and Liang Wang

    ACM Transactions on Intelligent Systems and Technology (ACM TIST), 2021


    2018-2020


  • Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions [paper]

    Feng Yu, Zhaocheng Liu, Qiang Liu, Haoli Zhang, Shu Wu and Liang Wang

    ACM International Conference on Information and Knowledge Management (CIKM), 2020


  • TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation [paper] [arXiv] [code]

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

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2020


  • TFNet: Multi-Semantic Feature Interaction for CTR Prediction [paper] [arXiv]

    Shu Wu, Feng Yu, Xueli Yu, Qiang Liu, Liang Wang, Tieniu Tan, Jie Shao and Fan Huang

    International ACM SIGIR conference on Research and Development in Information Retrieval (SIGIR), 2020


  • Deep Graph Contrastive Representation Learning [arXiv] [code]

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

    ICML Workshop on Graph Representation Learning and Beyond (GRL+@ICML), 2020


  • MV-RNN: A multi-view recurrent neural network for sequential recommendation [paper] [arXiv] [code]

    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]

    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


    2015-2017


  • 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] [code]

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

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


  • Contextual operation for recommender systems [paper]

    Shu Wu*, Qiang Liu*, Liang Wang and Tieniu Tan

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


  • SAPE: A system for situation-aware public security evaluation [paper]

    Shu Wu, Qiang Liu, Ping Bai, Liang Wang and Tieniu Tan

    AAAI Conference on Artificial Intelligence (AAAI), 2016


  • Information credibility evaluation on social media [paper] [arXiv]

    Shu Wu, Qiang Liu, Yong Liu, Liang Wang and Tieniu Tan

    AAAI Conference on Artificial Intelligence (AAAI), 2016


  • 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


  • 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


  • COT: Contextual operating tensor for context-aware recommender systems [paper]

    Qiang Liu, Shu Wu and Liang Wang

    AAAI Conference on Artificial Intelligence (AAAI), 2015