1. J. Su, C. Han, Y. Zhou, J. Shan, X. Zhou, F. Yuan^#.SaProt: Protein Language Modeling with Structure-aware Vocabulary. ICLR2024.

2. Y. He, X. Zhou, C. Chang, G. Chen, W. Liu, G. Li, X. Fan, Y. Ma, F. Yuan^#, X. Chang^#. Protein language models-assisted engineering of Uracil-N glycosylase enables programmable T-to-G and T-to-C base editing. Molecular Cell2024.

3. J. Zhang, Y. Cheng, Y. Ni, Y. Pan, F. Yuan^#. NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation. TPAMI2024.

4. Y. Cheng, Y. Pan, J. Zhang, Y. Ni, F. Yuan^#. An Image Dataset for Benchmarking Recommender Systems with Raw Pixels. SDM2024.

5. Y. Li, H. Du, Y. Ni, P. Zhao, Q. Guo^#, F. Yuan^#, X. Zhou, etc. Multi-Modality is All You Need for Transferable Recommender Systems. ICDE2024.

6. J. Fu, F. Yuan^#, Y. Song, Z. Yuan, M. Cheng, etc. Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights. WSDM2024.

7. Z. Yuan*, F. Yuan*^#, Y. Song, etc. Where to Go Next for Recommender Systems? ID- vs.Modality-based recommender models revisited. SIGIR2023.

8. G. Yuan*, F. Yuan*^#, B. Kong, etc. Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems. NeurIPS2022.

9. M. Hu* ，F. Yuan*^#, K. Yang, etc. Exploring evolution-based & -free protein language models as protein function predictors. NeurIPS2022.

10. 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.

11. 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.

12. 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.

13. 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.

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

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

16. 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.

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

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

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

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

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

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

23. 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)