Yingqiang Ge (葛英强)

About Me

Hi there, Welcome!

Currently, I’m a PhD student in Computer Science Department at Rutgers University, New Brunswick, NJ. And I am fortunate enough to have Prof. Yongfeng Zhang as my supervisor.

My research interests lie in the interface of Machine Learning and Information Retrieval with specific focus on: Recommender System, Economic Recommendation, Explainable Recommendation, Fairness, Reinforcement Learning for Recommendation, Causality.

News

  • 2021-10-11 Our research on “Pareto Efficient Fairness-Utility Trade-off” has been accepted as full paper by WSDM 2022.
  • 2021-08-09 Our proposal “Fairness of Machine Learning in Recommender Systems” has been accepted for presentation as a tutorial at the CIKM 2021.
  • 2021-08-08 Our research on “Counterfactual explainable recommendation” has been accepted by CIKM 2021.
  • 2021-04-27 Our proposal “Fairness of Machine Learning in Recommender Systems” has been accepted for presentation as a tutorial at the SIGIR 2021.
  • 2021-04-14 Our research on Personalized Fairness is accepted by SIGIR 2021.
  • 2021-01-16 Papers on Fairness, Generative Recommendadtion, Knowledge Graph Embedding accepted to WWW 2021.
  • 2020-10-16 Our research on Long-term Fairness is accepted as a full paper to the WSDM 2021 conference.
  • 2020-08-10 Our research on Neural Symbolic Reasoning for Explainable Recommendadtion is accepted by the CIKM 2020 conference.
  • 2020-06-20 Our research on Risk-aware Recommendation, Fairness-aware Recommendation, and Echo Chamber Analysis are accepted as long papers on the SIGIR 2020 conference.
  • 2019-01-20 Our recent research on bridging machine learning and economic principles for economic analysis of web-based systems is accepted by the Web Conference 2019 (WWW 2019).