Biography

I got my Ph.D. degree from University of Amsterdam (UvA), supervised by Prof. dr. Maarten de Rijke , Dr. Herke van Hoof, and Dr. Harrie Oosterhuis. Prior to that, I received my BE and ME degree in computer science from Renmin University of China under the supervision of Prof. dr. Xin Zhao and Prof. dr. Ji-Rong Wen in 2016 and 2019, respectively.

My research interest focuses on trustworthy intelligent information systems, with a focus on unbiasedness, fairness, robustness, and explainability.

I successfully defended my Ph.D. thesis titled Learning Recommender Systems from Biased User Interactions on 7th Feb 2024. I am looking for positions to continue my research.

Download my resumé.

Interests
  • Recommender systems
  • Information retrieval
  • Bias and debiasing
  • Reinforcement learning
Education

  • PhD in Computer Science, 2019 ~ 2024

    University of Amsterdam

  • MEng in Computer Science, 2016 ~ 2019

    Renmin University of China

  • BSc in Computer Science, 2012 ~ 2016

    Renmin University of China

Publications

Ming Li, Jin Huang, Maarten de Rijke (2023). Repetition and Exploration in Offline Reinforcement Learning-based Recommendations. In DRL4IR@CIKM workshop.

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Jin Huang, Harrie Oosterhuis, Bunyamin Cetinkaya, Thijs Rood, Maarten de Rijke (2022). State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. In SIGIR.

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Jin Huang, Harrie Oosterhuis, Maarten de Rijke (2022). It Is Different When Items Are Older: Debiasing Recommendations When Selection Bias and User Preferences are Dynamic. In WSDM.

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Jin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof (2020). Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender Systems. In RecSys.

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Wayne Xin Zhao, Gaole He, Kunlin Yang, Hongjian Dou, Jin Huang, Siqi Ouyang, Ji-Rong Wen (2019). KB4Rec: A Data Set for Linking Knowledge Bases with Recommender Systems. In Data Intelligence.

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Jin Huang, Zhaochun Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, Daxiang Dong (2019). Taxonomy-Aware Multi-Hop Reasoning Networks for Sequential Recommendation. In WSDM.

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Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, Edward Y. Chang (2018). Improving sequential recommendation with knowledge-enhanced memory networks. In SIGIR.

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Wayne Xin Zhao, Jin Huang, Ji-Rong Wen (2016). Learning Distributed Representations for Recommender Systems with a Network Embedding Approach. In Asia Information Retrieval Symposium (AIRS).

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Experiences

Teaching and Working

Teaching Assistant

  • Recommender Systems (June 2023) – MSc Artificial Intelligence
    • University of Amsterdam, Amsterdam, The Netherlands
    • Designed and gave letures; Supervised students to reproduce recommendation methods.
  • Information Retrieval (Feb. 2023 - March 2023) – MSc Artificial Intelligence
    • University of Amsterdam, Amsterdam, The Netherlands
    • Graded exercises and exams; Answered students’ questions.
  • Reinforcement Learning (Sep. 2021 - Oct. 2021) – MSc Artificial Intelligence
    • University of Amsterdam, Amsterdam, The Netherlands
    • Designed and graded exercises and exams; Answered students’ questions.
  • Reinforcement Learning (Sep. 2020 - Oct. 2020) – MSc Artificial Intelligence
    • University of Amsterdam, Online
    • Designed and graded exercises and exams; Answered students’ questions.

Student Supervision

  • Margot Pauelsen (BSc., March 2023 – June 2023)
    • Title: Towards Recommending Method Optimisation: The Effect of Optimisers on Collaborative Filtering Learning
  • Calvin Law (BSc., March 2023 – June 2023)
    • Title: Investigating the effect of different optimizers on Out-of-distribution scenarios in Recommender Systems
  • Cas Hortensius (MSc., March 2022 – June 2022)
    • Title: Comparing Collaborative Filtering Recommender Systems for the Hospitality Industry;
  • Helma Koopmans (MSc., Feb. 2021 – Feb. 2022)
    • Title: Fairness in two-sided markets;
  • Thijs Rood (BSc., April – June 2021)
    • Title: Attention-based State Encoder in Reinforcement Learning for Recommendation;
  • Bunyamin Çetinkaya (BSc., April – June 2021)
    • Title: Improving Reinforcement Learning for Recommendation Systems with a Convolutional Neural Network-based State Encoder
  • Luke de Keijzer (MSc., March – July 2020)
    • Title: Improving Company “Look-a-Likes” Finding Algorithm with the use of Graph Theory.

Internship

  • Applied Scientist Intern at Amazon – Seattle, USA, July 2022 – Nov. 2022
    • Worked on decomposing complex user goals into a series of simple sub-questions.
  • Research Intern at Microsoft Research Asia – Beijing, China, May 2018 – Nov. 2018
    • Worked on designing a Knowledge Graph-based Recommender System for a financial institution and Meta-path based Recommender system.

Professional Activities

Workshop Organization

Tutorial Organization

Reviewer

  • Journal:
    • ACM Transactions on Intelligent Systems and Technology (TIST)
    • ACM Transactions on Information Systems (TOIS)
    • Information Processing and Management (IPM)