Fajie YUAN, Ph.D.

School of Engineering

Lab for Representation Learning

CONTACT

Email: yuanfajie@westlake.edu.cn

Website: https://fajieyuan.github.io

Fajie YUAN, Ph.D.

School of Engineering

Lab for Representation Learning

CONTACT

Email: yuanfajie@westlake.edu.cn

Website: https://fajieyuan.github.io

"It is my great honor to join Westlake. I will do my best to conduct world-class research, and be both a teacher and helpful friend to my students."

Biography

Before joining Westlake, Fajie was a senior AI researcher at Tencent, working on recommender systems and user modeling. In Nov 2018, he obtained his Ph.D. degree at University of Glasgow, advised by Prof. Joemon Jose. Between 2017 and 2018, He was also a visiting scholar at National University of Singapore, supported by Jim Gatheral Travel Scholarship, and research intern at Telefonic Research in Barcelona, mentored by Dr. Alexandros Karatzoglou and Dr. Ioannis Arapakis. He will formally join Westlake University in April 2021 as an assistant professor, focusing on two major research directions: deep user representation learning (for personalized recommender systems) and AI+Life Science.


Research

His current research has been mainly in deep learning and its various applications, such as personalized recommender systems, user representation learning, NLP and Life AI. He has published over 10 research papers in premier AI conference (including UAI, SIGIR, WWW, WSDM, ACL, etc.) as the first or co-first author. Several of his AI algorithms were applied in real production systems, such as LambdaFM (CIKM2016), NextItNet (WSDM2019), and PeterRec (SIGIR2020). In particular, NextItNet has also become a widely adopted baseline in the field of session-based recommender systems and achieved over 100 citations in two years.


Representative Publications

* denotes equal contribution

1. F. Yuan, G. Zhang, A. Karatzoglou, X. He, J. Jose, B. Kong, Y. Li.  One Person, One Model, One World: Learning Continual User Representation without Forgetting. SIGIR, 2021.

2. J. Wang*, F. Yuan*, J. Chen, Q. Wu, C. Li, M. Yang, Y. Sun, G. Zhang. StackRec: Efficient Training of Very Deep Sequential Recommender Models by Layer Stacking. SIGIR, 2021.

3. M. Chen*, F. Yuan*, Q. Liu, S. Ge, Z. Li, R. Yu, D. Lian, S. Yuan, En, Chen.Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement. SIGIR, 2021.

4. L. Chen*, F. Yuan*, J. Yang, X. Ao, C. L, M, Yang. SkipRec: A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models. AAAI2021.

5. F. Yuan, X. He, A. Karatzoglou, L. Zhang. Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation. SIGIR 2020.

6. Y. Sun*, F. Yuan*, M. Yang, G. Wei, Z. Zhao, D, Liu. A Generic Network Compression Framework for Sequential Recommender Systems. SIGIR2020.

7. F. Yuan, X. He, H.Jiang, G. Guo, J. Xiong, Z. Xu, Y. Xiong. Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation. WWW2020.

8. F. Yuan, A. Karatzoglou, I. Arapakis, J. Jose, X. He.  A Simple Convolutional Generative Network for Next Item Recommendation. WSDM2019. 

9. F. Yuan,X. Xin, X. He, G. Guo, W.Zhang, T. Chua, J. Jose.  fBGD: Learning Embeddings From Positive Unlabeled Data with BGD. UAI2018

10. X. Xin*, F. Yuan*, X. He, J. Jose. Batch IS NOT Heavy: Learning Word Representations From All Samples. ACL2018

11.G. Guo*, SC.Ouyang*,F. Yuan*. Approximating Word Ranking and Negative Sampling for Word Embedding. IJCAI2019

12. F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W.Zhang.  BoostFM: Boosted Factorization Machines for top-N Feature-based Recommendation. ACM IUI2017

13. F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W.Zhang. LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates. CIKM2016

14. F. Yuan, J. Jose, G. Guo, L. Chen, H. Yu, R. Alkhawaldeh. Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation. ICTAI 2016 (Best Student Paper)