Yin Zhang

  Department of Computer Science and Engineering
  Texas A&M University
  Infolab
  Office: Bright Building (HRBB)
  E-mail: zhan13679 (at)tamu.edu

About Me

Howdy! I am a PhD student in Computer Science at Texas A&M University advised by Dr. James Caverlee and work at the infolab Lab. I got my Master degree in the Department of Statistics at University of California, Davis. I received a B.S. in Department of Mathematics and minored in Psychology at Beijing Normal University in 2014. Please find my CV here.

My current research focuses on recommender systems, specifically the complementary item recommendation and fashion recommendation. I am also working on how to effictively utilize visual and textual information for recommendation methods.

Visual Evolution and Fashion Recommendation

It is one of my current projects. Visual preferences are dynamically changed over time. Fashion bloggers [wiki], as both fashion experts and fashion key influencers, can link high fashion with our daily wear through their appealing visual posts. They play a significant role in fashion adoption over time. Those fashion bloggers also own millions of followers in social media, which shows their potential huge influence. Their visual posts contain both high-quality fashion features and show users dynamic aesthetic preference over time. We utilize the fashion blogger's visual posts overtime to capture the visual evolution and give a fashion recommendation for users. Details please see our paper ``Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence'' [CIKM19].

  • Dataset of Fashion Blogger Dynamic Visual Information: In the paper, we provide a time-aware aesthetic high-quality dataset — more than 130,000 Instagram time-aware visual posts from influential female fashion bloggers. The dataset is available upon your request.

Publications

  • A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation.
  • Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi, Lichan Hong, Ed H. Chi.
    The Web Conference (WWW 2021).
    [PDF][Slides].

  • Item Relationship Graph Neural Networks for E-commerce.
  • Weiwen Liu, Yin Zhang, Jianling Wang, Yun He, James Caverlee, Patrick P. K. Chan, Daniel S. Yeung, and Pheng-Ann Heng.
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS 2021).
    [PDF][Slides].

  • Content-Collaborative Disentanglement Representation Learning for Enhanced Recommendation.
  • Yin Zhang, Ziwei Zhu, Yun He, James Caverlee.
    The 14th ACM Conference on Recommender Systems (RecSys 2020). (acceptance rate: 18% )
    [PDF][Slides].

  • Adaptive Hierarchical Translation-based Sequential Recommendation (with Temporal Graph).
  • Yin Zhang, Yun He, Jianling Wang, James Caverlee.
    The Web Conference (WWW 2020).
    [PDF][Slides].

  • PARADE: A New Dataset for Paraphrase Identification Requiring Computer Science Domain Knowledge.
  • Yun He, Zhuoer Wang, Yin Zhang, Ruihong Huang, James Caverlee.
    Empirical Methods in Natural Language Processing (EMNLP 2020).
    [PDF].

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

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

  • Popularity-Opportunity Bias in Collaborative Filtering.
  • Ziwei Zhu, Yun He, Xing Zhao, Yin Zhang, Jianling Wang, James Caverlee.
    International Conference on Web Search and Data Mining (WSDM 2021).
    [PDF].

  • Instagrammers, Fashionistas, and Me: Recurrent Fashion Recommendation with Implicit Visual Influence.
  • Yin Zhang and James Caverlee.
    The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019).
    [PDF][Slides](The dataset is available upon your request).

  • Consistency-Aware Recommendation for User-Generated Item Lists Continuation.
  • Yun He, Yin Zhang, Weiwen Liu, James Caverlee.
    International Conference on Web Search and Data Mining (WSDM 2020).
    [PDF]

  • Improving the Estimation of Tail Ratings in Recommender System .
  • Xing Zhao, Ziwei Zhu, Yin Zhang, James Caverlee.
    International Conference on Web Search and Data Mining (WSDM 2020).
    [PDF]

  • Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion.
  • Jianling Wang*, Kaize Ding*, Ziwei Zhu, Yin Zhang, James Caverlee. (*equal contribution).
    International Conference on Web Search and Data Mining (WSDM 2020).
    [PDF]

  • An Interpretable Neural Model with Interactive Stepwise Influence.
  • Yin Zhang, Ninghao Liu, Shuiwang Ji, James Caverlee, and Xia Hu.
    The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2019).
    [PDF][Code]

  • Quality-Aware Neural Complementary Item Recommendation.
  • Yin Zhang, Haokai Lu, Wei Niu and James Caverlee.
    The 12th ACM Recommender Systems Conference (RecSys 2018). (acceptance rate: 17.7% )
    [PDF][Slides][Code]

  • Fairness-Aware Recommendation of Information Curators.
  • Ziwei Zhu, Jianling Wang, Yin Zhang, James Caverlee.
    The 12th ACM Recommender Systems Conference Workshop (RecSys Workshop 2018).
    [PDF]

  • Set Pair Analysis Based on Phase Space Reconstruction Model and Its Application in Forecasting Extreme Temperature.
  • Yin Zhang, Xiaohua Yang, Ling Zhang, et al.
    Mathematical Problems in Engineering, 2013, 2013. (SCI, IF:1.383)
    [PDF]

  • Analysis model for forecasting extreme temperature using refined rank set pair.
  • Lingxia Qiao, Yin Zhang, et al.
    Thermal Science 17.5 (2013): 1369-1374. (SCI, IF: 1.45)
    [PDF]

Industry Experience

  • Research Intern, Google Inc., Google Brain.
    May - Sep, 2020.
  • Research Intern, Pinterest, Pinterest lab, Ads Retrieval Team.
    Project: User short-term intent based on sequential models, May - Aug, 2019.
  • Data Scientist Intern, Workday, Data Scientist Team.
    Project: Sales analysis, June - Aug, 2016.

Teaching Experience

  • Teaching assistant, worked with Dr. James Caverlee, in CSCE 470 Information Retrieval - undergraduate level course offered (Sep, 2018).
  • Teaching assistant, worked with Dr. Piabir Burman, in STA 137 - Applied Time Series Analysis - undergraduate level course offered (Sep, 2016).

Professional Services

  • Journal Reviewer: Transactions on Knowledge and Data Engineering (TKDE), IEEE Intelligent Systems, The International Journal on Very Large Data Bases (VLDBJ)
  • Sub-Reviewer: WSDM'21
  • External Reviewer: WWW, WSDM, KDD, SDM
  • Session Chair: CIKM 2019 for Deep Neural Network I Session

Selected Awards

  • The Web Conference Scholarship, 2020
  • The Grace Hopper Celebration Scholarship, 2020
  • CSE Deparment Travel Awards in TAMU, 2020
  • CIKM Travel Awards, 2019
  • CSE Deparment Travel Awards in TAMU, 2019
  • National Second Prize, National College Mathematical Contest in Modeling (more than 70000 college student took part, 6.5% nationwide), 2012
  • Honorable Prize, American Mathematical Contest in Modeling (ICM)(40% worldwide), 2013
  • First prize of Academic Scholarship, 2012-2013
  • Second prize of Contest Scholarship, 2012-2013
  • Outstanding Undergraduate Research Fund program in BNU, 2013
  • Outstanding National Training Programs of Innovation and Entrepreneurship in BNU, 2014

Last updated: Mar. 2021