Ziwei Zhu


Howdy! I am a fifth-year Ph.D. candidate in the Computer Science & Engineering Department at Texas A&M University, advised by Prof. James Caverlee. I'm broadly interested in the area of data mining, machine learning and information retrieval. Specifically, my research focuses on:
  • Fairness and bias in recommender systems, such as: fairness in tensor-based recommendation [CIKM 2018]; fairness in personalized ranking recommendation [SIGIR 2020]; and equal opportunity based popularity bias in recommendation [WSDM 2021].
  • Advanced machine leanring/deep leanring techniques for improving recommendation quality, such as: better top-k recommendation via joint autoencoder [WWW 2019]; better cold start recommendation by randomized training and mixture-of-experts [SIGIR 2020]; and better recommendation with MNAR feedback via combinational joint learning [RecSys 2020].
I obtained my Bachelor's degree in Computer Science from Wuhan University in China before coming to TAMU.

Publications

2021

  • [KDD 2021] Popularity Bias in Dynamic Recommendation. [pdf] [code] [slides] [poster]
    The 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2021.
    Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee.

  • [SIGIR 2021] Fairness among New Items in Cold Start Recommender Systems. [pdf] [code] [slides]
    The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021.
    Ziwei Zhu, Jingu Kim, Trung Nguyen, Aish Fenton, and James Caverlee.

  • [WWW 2021] Rabbit Holes and Taste Distortion: Distribution-Aware Recommendation with Evolving Interests. [pdf]
    The 32th International Conference on World Wide Web, 2021.
    Xing Zhao, Ziwei Zhu, and James Caverlee.

  • [SDM 2021] Session-based Recommendation with Hypergraph Attention Networks. [pdf]
    The 2021 SIAM International Conference on Data Mining, 2021.
    Jianling Wang, Kaize Ding, Ziwei Zhu, and James Caverlee.

  • [WSDM 2021] Popularity-Opportunity Bias in Collaborative Filtering. [pdf] [slides] [poster]
    The 14th ACM International Conference on Web Search and Data Mining, 2021.
    Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, and James Caverlee.

2020

  • [EMNLP 2020] Infusing Disease Knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition. [pdf] [code]
    The 2020 Conference on Empirical Methods in Natural Language Processing.
    Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, and James Caverlee.

  • [RecSys 2020] Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint Learning (short paper). [pdf] [code] [poster]
    The 14th ACM Conference on Recommender Systems, 2020.
    Ziwei Zhu, Yun He, Yin Zhang, and James Caverlee.

  • [RecSys 2020] Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation. [pdf] [slides]
    The 14th ACM Conference on Recommender Systems, 2020.
    Yin Zhang, Ziwei Zhu, Yun He, and James Caverlee.

  • [SIGIR 2020] Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems. [pdf] [code] [SIGIR slides] [method slides] [arxiv]
    The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
    Ziwei Zhu, Jianling Wang and James Caverlee.

  • [SIGIR 2020] Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. [pdf] [code] [slides]
    The 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020.
    Ziwei Zhu, Shahin Sefati, Parsa Saadatpanah and James Caverlee.

  • [WWW 2020] Addressing the Target Customer Distortion Problem in Recommender Systems. (short paper) [pdf]
    The 31th International Conference on World Wide Web, 2020.
    Xing Zhao, Ziwei Zhu, Majid Alfifi and James Caverlee.

  • [WSDM 2020] Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations. [pdf]
    The 13th ACM International Conference on Web Search and Data Mining, 2020.
    Xing Zhao, Ziwei Zhu, Yin Zhang and James Caverlee.

  • [WSDM 2020] User Recommendation in Content Curation Platforms. [pdf] [code] [slides]
    The 13th ACM International Conference on Web Search and Data Mining, 2020.
    Jianling Wang, Ziwei Zhu and James Caverlee.

  • [WSDM 2020] Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion. [pdf] [poster]
    The 13th ACM International Conference on Web Search and Data Mining, 2020.
    Jianling Wang, Kaize Ding, Ziwei Zhu, Yin Zhang and James Caverlee.

2019

  • [WWW 2019] Improving Top-K Recommendation via Joint Collaborative Autoencoders. (short paper) [pdf] [code] [poster]
    The 30th International Conference on World Wide Web, 2019.
    Ziwei Zhu, Jianling Wang, and James Caverlee.

2018

  • [CIKM 2018] Fairness-Aware Tensor-Based Recommendation. [pdf] [slides] [code]
    The 27th ACM International Conference on Information and Knowledge Management, 2018.
    Ziwei Zhu, Xia Hu, and James Caverlee.

  • [FATREC 2018] Fairness-Aware Recommendation of Information Curators [pdf]
    The 2nd FATREC Workshop on Responsible Recommendation at RecSys, 2018.
    Ziwei Zhu, Jianling Wang, Yin Zhang, and James Caverlee.

  • [ICDM 2018] Pseudo-Implicit Feedback for Alleviating Data Sparsity in Top-K Recommendation. (short paper) [pdf] [code]
    The 2018 IEEE International Conference on Data Mining, 2018.
    Yun He, Haochen Chen, Ziwei Zhu, and James Caverlee.

2017

  • [BSN 2017] Modeling and Detecting Student Attention and Interest Level Using Wearable Computers. [pdf]
    IEEE International Conference on Wearable and Implantable Body Sensor Networks, 2017.
    Ziwei Zhu, Sebastian Ober, Roozbeh Jafari.

Industry Experience

Teaching

  • Guest Lecturer: CSCE 489 Special Topics in Recommender Systems, TAMU, Spring, 2021

  • Teaching Assistant: CSCE 489 Special Topics in Recommender Systems, TAMU, Spring, 2021

  • Teaching Assistant: CSCE 676 Data Mining and Analysis, TAMU, Fall, 2019

  • Teaching Assistant: CSCE 206 Structured Programming in C, TAMU, Fall, 2017

Technical Talks

  • Toward Fairness-aware Recommender Systems. University of North Texas, online, 2021.
  • Popularity-Opportunity Bias in Collaborative Filtering. WSDM, online, 2021.
  • Item Fairness in Recommender Systems. Netflix Research Seminar, online, 2020.
  • Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. SIGIR, online, 2020.
  • Measuring and Mitigating Item Under- Recommendation Bias in Personalized Ranking Systems. SIGIR, online, 2020.
  • Fairness-Aware Tensor-Based Recommendation. CIKM, Turin, Italy, 2018.

Professional Services

  • Journal Reviewer: IEEE Transactions on Knowledge and Data Engineering, IEEE Intelligent Systems, Information Retrieval Journal, IEEE Transactions on Services Computing, Transactions on Information Systems, Information Processing and Management.
  • Conference Reviewer:
  • External Reviewer: WWW'18, WWW'19.

Selected Awards

  • WSDM Student Travel Grant, 2021
  • SIGIR Student Travel Grant, 2020
  • CIKM Student Travel Grant, 2018
  • National Scholarship, 2013
  • Top Level Scholarship for Excellent Student, 2014