
Ziwei Zhu
- Ph.D. student
- Computer Science & Engineering Department
- Texas A&M University
- Office: 408A, H.R. Bright Building, College Station, TX, 77843
- Email: zhuziwei 'at' tamu.edu
- Resume, Google Scholar,
Howdy! I am a fourth-year Ph.D. student 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 recommender systems. 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].
Publications
2021
- [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]
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]
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
- Research Intern, Netflix Research, CA, USA, May 2020 - Aug 2020.
- Research Intern, Comcast Applied AI Lab, DC, USA, May 2019 - Aug 2019.
- Software engineering intern, Analysys, Beijing, China, Jun 2015 - Aug 2015.
Teaching
- Teaching Assistant: CSCE 676 Data Mining and Analysis, TAMU, Fall, 2019
- Teaching Assistant: CSCE 206 Structured Programming in C, TAMU, Fall, 2017
Technical Talks
- Recommendation for New Users and New Items via Randomized Training and Mixture-of-Experts Transformation. SIGIR, Xi'an, China, 2020.
- Measuring and Mitigating Item Under- Recommendation Bias in Personalized Ranking Systems. SIGIR, Xi'an, China, 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.
- Conference Reviewer:
- External Reviewer: WWW'18, WWW'19.
Selected Awards
- National Scholarship in 2013
- Top Level Scholarship for Excellent Student in 2014