Ziwei Zhu

Ziwei Zhu

(竺子崴)

Howdy! I am an Assistant Professor in the Department of Computer Science at George Mason University. I got my Ph.D. from the Computer Science & Engineering Department at Texas A&M University, advised by Prof. James Caverlee. Before that, I obtained my Bachelor's degree in Computer Science from Wuhan University.

I am broadly interested in data mining, machine learning, and information retrieval, with a special emphasis on augmenting responsibility in machine learning to provide fair, unbiased, accountable, and trustworthy information services for both end users and society-at-large.

I am looking for multiple PhD students. If you are interested, please contact me.

For Prospective Students

For prospective PhD students: I am looking for highly self-motivated PhD students with funding support. If you are interested in working with me, please email me with your CV, transcript, a description of your research interests and experience, and your writting samples (e.g., publications, dissertations, project reports, and so on). Please start your email subject with "[Prospective PhD Student - ${Your_Application_Semester}]".

News

  • 02/2024: Invited to be a PC member for ECMLPKDD 2024.
  • 01/2024: One paper about machine unlearning is accepted to WWW 2024, congratulations to all collaborators!
  • 01/2024: Invited to be a PC member for KDD 2024.
  • 12/2023: Three papers are accepted to ECIR 2024 IR4Good track, congratulations to all collaborators!
  • 12/2023: A paper about cross-domain recommendation is accepted to SDM 2024, congratulations to Ajay!
  • 11/2023: Invited to be a PC member for PAKDD 2024.
  • 10/2023: Gave talks about fairness in RecSys at American University and VT
  • 10/2023: A paper about societal biases in multilingual LM is accepted to EMNLP 2023, congratulations to my student Anjishnu and Chahat!
  • 10/2023: Two papers are accepted to EMNLP Findings 2023, congratulations to Zhuoer, Xiangjue, and Cav!
  • 09/2023: Received a funding award from 4-VA@GMU, thank you!
  • 09/2023: Invited to be a PC member for AAAI 2024.
  • 09/2023: Invited to be a PC member for SDM 2024.
  • 08/2023: A paper about unbiased post-click conversion prediction is accepted to CIKM 2023, congratulations to my student Yuqing!
  • 07/2023: A paper about alleviating filter bubbles in dynamic news recommendation is accepted to KDD EAI Workshop.
  • 06/2023: Invited to be a PC member for WSDM 2024.
  • 06/2023: Invited to be a PC member for CIKM 2023.
  • 05/2023: A paper about attacks to dialog systems is accepted to ACL Findings 2023.
  • 04/2023: Invited to be a PC member for RecSys 2023.
  • 02/2023: A paper about Mobile Voice Assistant for Emergency Medical Services is accepted to MobiSys 2023.
  • 02/2023: Invited to be a PC member for ECMLPKDD 2023.
  • 01/2023: A paper about conversational RecSys is accepted to WWW 2023.
  • 01/2023: Invited to be a PC member for FAccT 2023.
  • 01/2023: Invited to be a PC member for KDD 2023.
  • 12/2022: A short paper about polarization in news recommender system is accepted to ECIR 2023.
  • 09/2022: Invited to be a PC member for SDM 2023.
  • 08/2022: Received the best paper award at the DSAI4RRS workshop!
  • 08/2022: A long paper about popularity bias in conversational recommender system is accepted to CIKM 2022.
  • 07/2022: Invited to be a PC member for AAAI 2023.
  • 07/2022: A paper about recommendation popularity bias is accepted to DSAI4RRS workshop. See you in August!
  • 06/2022: Invited to be a PC member for WSDM 2023.
  • 05/2022: Successfully defended my Ph.D. dissertation!
  • 03/2022: I am so happy to receive the Graduate Research Excellence Award from the CSE department at TAMU!
  • 01/2022: A paper about fairness in learning-to-rank is accepted to WWW 2022 Web4Good track.
  • 11/2021: Invited to be a PC member for KDD 2022 Applied Science Track.
  • 10/2021: A long paper about user mainstream bias in recommendation is accepted to WSDM 2022.
  • 08/2021: Gave an oral presentation for our accepted paper at KDD 2021.
  • 07/2021: Gave an oral presentation for our accepted paper at SIGIR 2021.
  • 07/2021: Gave a talk about recommendation fairness among cold-start items at Netflix research seminar.
  • 06/2021: Invited to be a PC member for WSDM 2022.
  • 05/2021: A long paper about popularity bias in dynamic recommendation is accepted to KDD 2021.
  • 04/2021: Gave a talk about fairness in recommender systems at University of North Texas.
  • 04/2021: A long paper about recommendation fairness among cold-start items is accepted to SIGIR 2021.

Students

  • Chahat Raj, PhD, Fall 2022 -
  • Anjishnu Mukherjee, PhD, Fall 2022 -
  • Yuqing Zhou, PhD, Fall 2023 -
  • Bowen Wei, PhD, Fall 2023 -
  • Mehrdad Fazli, PhD, Fall 2023 -
  • Balassubramanian Srinivasan, MS
  • Rishi Pania, Undergrad
  • Tanvi Pedireddi, Thomas Jefferson High School
  • Amrit Singh, Thomas Jefferson High School
  • Publications

    (Google Scholar) (Semantic Scholar)
    • [WWW 2024] Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning.
      The 2024 ACM Web Conference, 2024.
      Zheyuan Liu, Guangyao Dou, Eli Chien, Chunhui Zhang, Yijun Tian, Ziwei Zhu
    • [ECIR IR4Good 2024] SALSA: Salience-Based Switching Attack for Adversarial Perturbations in Fake News Detection Models.
      46th European Conference on Information Retrieval, 2024.
      Chahat Raj, Anjishnu Mukherjee, Hemant Purohit, Antonios Anastasopoulos, Ziwei Zhu
    • [ECIR IR4Good 2024] End-to-End Adaptive Local Learning for Alleviating Mainstream Bias in Collaborative Filtering.
      46th European Conference on Information Retrieval, 2024.
      Jinhao Pan, Ziwei Zhu, Jianling Wang, Allen Lin, James Caverlee
    • [ECIR IR4Good 2024] Federated Conversational Recommender Systems.
      46th European Conference on Information Retrieval, 2024.
      Allen Lin, Jianling Wang, Ziwei Zhu, James Caverlee
    • [SDM 2024] Vietoris-Rips Complex: A New Direction for Cross-Domain Cold-Start Recommendation.
      The 2024 SIAM International Conference on Data Mining, 2024.
      Ajay Krishna Vajjala, Dipak Falgun Meher, Shrunal Pothagoni, Ziwei Zhu, David S. Rosenblum
    • [EMNLP 2023] Global Voices, Local Biases: Socio-Cultural Prejudices across Languages.
      The 2023 Conference on Empirical Methods in Natural Language Processing.
      Anjishnu Mukherjee*, Chahat Raj*, Ziwei Zhu, and Antonios Anastasopoulos.
    • [EMNLP 2023 Findings] Co^2PT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning.
      The 2023 Conference on Empirical Methods in Natural Language Processing.
      Xiangjue Dong, Ziwei Zhu, Zhuoer Wang, Maria Teleki, and James Caverlee.
    • [EMNLP 2023 Findings] Unsupervised Candidate Answer Extraction through Differentiable Masker-Reconstructor Model.
      The 2023 Conference on Empirical Methods in Natural Language Processing.
      Zhuoer Wang, Yicheng Wang, Ziwei Zhu, and James Caverlee.
    • [CIKM 2023] A Generalized Propensity Learning Framework for Unbiased Post-Click Conversion Rate Estimation.
      The 32nd ACM International Conference on Information and Knowledge Management.
      Yuqing Zhou, Tianshu Feng, Mingrui Liu, and Ziwei Zhu.
    • [EAI 2023] Alleviating Filter Bubbles and Polarization in News Recommendation via Dynamic Calibration.
      2nd ACM SIGKDD Workshop on Ethical Artificial Intelligence: Methods and Applications.
      Han Zhang, Ziwei Zhu, and James Caverlee.
    • [ACL 2023 Findings] PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts.
      Findings of the Association for Computational Linguistics 2023.
      Xiangjue Dong, Yun He, Ziwei Zhu, James Caverlee.
    • [MobiSys 2023] EMSAssist: An End-to-End Mobile Voice Assistant at the Edge for Emergency Medical Services.
      The 21st ACM International Conference on Mobile Systems, Applications, and Services, 2023.
      Liuyi Jin, Tian Liu, Amran Haroon, Radu Stoleru, Michael Middleton, Ziwei Zhu, Theodora Chaspari.
    • [WWW 2023] Enhancing User Personalization in Conversational Recommenders.
      The 2023 ACM Web Conference, 2023.
      Allen Lin, Ziwei Zhu, Jianling Wang, and James Caverlee.
    • [ECIR 2023] Evolution of Filter Bubbles and Polarization in News Recommendation. (short paper)
      The 45th European Conference on Information Retrieval, 2023.
      Han Zhang, Ziwei Zhu, and James Caverlee.
    • [CIKM 2022] Quantifying and Mitigating Popularity Bias in Conversational Recommender Systems.
      The 31st ACM International Conference on Information and Knowledge Management, 2022.
      Allen Lin, Jianling Wang, Ziwei Zhu, and James Caverlee.
    • [FAccTRec 2022] Towards Fair Conversational Recommender Systems.
      The 5th FAccTRec Workshop on Responsible Recommendation at RecSys 2022.
      Allen Lin, Ziwei Zhu, Jianling Wang, and James Caverlee.
    • [DSAI4RRS 2022] Evolution of Popularity Bias: Empirical Study and Debiasing. [Best Paper Award] [pdf]
      KDD 2022 Workshop on Data Science and Artificial Intelligence for Responsible Recommendations (DS4RRS), 2022.
      Ziwei Zhu, Yun He, Xing Zhao, and James Caverlee.
    • [WWW Web4Good 2022] End-to-end Learning for Fair Ranking Systems. [pdf]
      The Web4Good special track in 33rd ACM International Conference on World Wide Web, 2022.
      James Kotary, Ferdinando Fioretto, Pascal Van Hentenryck, and Ziwei Zhu.
    • [WSDM 2022] Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning. [pdf] [code]
      The 15th ACM International Conference on Web Search and Data Mining, 2022.
      Ziwei Zhu and James Caverlee.
    • [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 32nd 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.
    • [SSL@WWW 2021] Infusing disease knowledge into BERT for Health Question Answering, Medical Inference and Disease Name Recognition.
      The Workshop on Self-Supervised Learning for the Web at WWW, 2021.
      Yun He, Ziwei Zhu, Yin Zhang, Qin Chen, and James Caverlee.
    • [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] [extended slides] [arxiv version]
      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 31st 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.
    • [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.
    • [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.
    • [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.

    Teaching

    • Lecturer: CS 584, Data Mining, GMU, Spring 2024
    • Lecturer: CS 782, Advanced Machine Learning, GMU, Fall 2023
    • Lecturer: CS 484, Data Mining, GMU, Spring 2023
    • Lecturer: CS 584, Data Mining, GMU, Fall 2022
    • 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

    Service

    • Conference program committees: WSDM (2022, 2023, 2024), KDD (2022, 2023, 2024), RecSys 2023, CIKM 2023, AAAI (2023, 2024), SDM (2023, 2024), ECMLPKDD (2023, 2024), FAccT 2023, PAKDD 2024
    • Journal reviewers: IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Services Computing, IEEE Intelligent Systems, ACM Transactions on Intelligent Systems and Technology, ACM Transactions on Recommender Systems, ACM Transactions on Information Systems, Information Processing and Management, Big Data Journal, The Electronic Library, Knowledge-based Systems, Machine Learning Journal, Heliyon Journal, Neurocomputing Journal, Science China Information Sciences Journal, International Journal of Human-Computer Interaction

    Invited Talks

    • Fairness in RecSys, VT, 10/2023
    • Fairness in RecSys, American University, 10/2023
    • Fairness in Artificial Intelligence, MPI ReConEx 2023, 04/2023
    • Toward Fairness-aware Recommender Systems, DEFirst Seminar, 03/2023
    • Fairness among New Items in Cold Start Recommender Systems, research seminar at Netflix, 07/2021
    • Toward Fairness-aware Recommender Systems, University of North Texas, 04/2021
    • Item Fairness in Recommender Systems, research seminar at Netflix, 08/2020