News
13th Oct2023
Our RA Yongxin gave a talk to Google DeepMind introducing our recent work. ID vs. Modality and Large Micro-Video Recsys Datasets！
20 Apr2018
A full paper about word embedding was accepted by ACL2018, equal contribution with Xinxin.
16 Apr2018
A full paper Approximating Word Ranking and Negative Sampling for Word Embedding was accepted by IJCAI2018, equal contribution with Guibing Guo and Shichang Ouyang.
10 Dec 2016
A full paper Boosted Factorization Machines for top-N Feature-based Recommendations was accepted by ACM IUI2017.
7 Nov. 2016
We won the Best Studeng Paper Award (5/157) in ICTAI16 after the presentation of our work.
29 Oct. 2016
I am travelling to San Jose for ICTAI16 and will present our work GeoBPR.
24 Oct. 2016
I am travelling to Indianapolis for CIKM16 and will present our work LambdaFM.
27 Sep. 2016
I am invited to be the reviewer of European Conference on Information Retrieval (ECIR 2017).
24 Aug. 2016
Our work Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation is accepted by ICTAI as a full paper.
14 July 2016
Our work LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates is accepted by CIKM as a full paper.
31 Aug. 2015
I am travelling to thessaloniki,Greece for The European Summer School in Information Retrieval (ESSIR 2015) .
Tweets by @duguyuan
Copyright@Webpage template is from Weinan Zhang.
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Fajie Yuan
Assistant Professor
AI division, Westlake University
Hangzhou, China
Email: yuanfajie[AT]westlake.edu.cn
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Before joining Westlake University, Fajie was a senior AI researcher at Tencent, working on recommender systems and AI foundation models. He obtained his Ph.D. degree at University of Glasgow, advised by Prof. Joemon Jose in 2018. 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 has published over 30 research papers in premier AI conferences as the first/co-first/corresponding author. Several of his AI algorithms were applied in real-world large-scale production systems, such as LambdaFM (CIKM2016), NextItNet (WSDM2019), and PeterRec (SIGIR2020). At Westlake, he worked as an independent PI, focusing on two major research directions: AI+Life Science and deep learning (for recommender systems) He often works as reviewers for premier AI conferences and bioinfomatics journals, such as NeurIPS, SIGIR, KDD, WSDM, CIKM, WWW, Recsys etc. His lab is now recruiting self-motivated interns / full-time (posdoc/assistant/associate) researchers/ PHD students in machine learning and Life AI. 西湖大学原发杰团队长期招聘：推荐系统和生物信息（尤其蛋白质相关）方向 ，科研助理，博士生 （2024年4月份招生仍有1个PHD名额），博后，访问学者，助理研究员系列。 可以提前联系我，如果你想要申请我的PHD！ He is also open to various collaborations. If you are interested in, please drop an email.
Talk
Large-scale Recommendation DataSets
Selected Publications [Google Scholar] [Github]
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SaprotHub: Making Protein Modeling Accessible to All Biologists
J. Su, Z. Li, C. Han, Y. Zhou, J. Shan, X. Zhou, T. OPMC, S. Ovchinnikov, F. Yuan# Preprint. Codes blog |
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Toward De Novo Protein Design from Natural Language
F. Dai, Y. Fan, J. Su, C. Wang, ..., F. Yuan# Preprint. Code (soon) |
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ProTrek: Navigating the Protein Universe through Tri-Modal Contrastive Learning
J. Su, X. Zhou, X. Zhang, F. Yuan# Preprint. Codes blog |
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SaProt: Protein Language Modeling with Structure-aware Vocabulary
J. Su, C. Han, Y. Zhou, J. Shan, X. Zhou, F. Yuan# 650M的蛋白质结构预训练大模型 ICLR2024 spotlight (top5%) Codes ranked first on Proteingym leaderboard |
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Protein language models-assisted engineering of Uracil-N glycosylase enables programmable T-to-G and T-to-C base editing
Y. He, X. Zhou, C. Chang, G. Chen, W. Liu, G. Li, X. Fan, Y. Ma, F. Yuan# , X. Chang# Molecular Cell (Impact factor：19.3) Code STAR Protocols |
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Generative Diffusion Models for Antibody Design, Docking, and Optimization
Z. Peng, C. Han, X. Wang, J. Shan, D. Li#, F. Yuan# Diffusion 模型抗体设计和优化 Preprint. Codes |
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An Image Dataset for Benchmarking Recommender Systems with Raw Pixels
Y. Cheng, Y. Pan, J. Zhang, Y. Ni, F. Yuan# 最大的信息流媒体封面图像推荐系统数据集 SDM (2024) Codes |
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NineRec: A Benchmark Dataset Suite for Evaluating Transferable Recommendation
J. Zhang, Y. Cheng, Y. Ni, Y. Pan, F. Yuan# 最多样的多模态推荐系统数据集用于迁移学习研究 TPAMI2024 (Impact factor：24.3) Codes |
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A Content-Driven Micro-Video Recommendation Dataset at Scale
Y. Ni, Y. Cheng, X. Liu, J. Fu, Y. Li, X. He, Y. Zhang F. Yuan# 首套大规模微视频推荐系统数据集，含有原始视频 Invited Talk (Google DeepMind) Codes |
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Exploring the Upper Limits of Text-Based Collaborative Filtering Using Large Language Models: Discoveries and Insights
R. Li, W. Deng, Yu. Cheng, Z. Yuan, J. Zhang, F. Yuan# . ID vs. GPT-3, 175B的GPT-3能否打败最简单的ID？ Preprint. Codes (Soon) |
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TransRec: Learning Transferable Recommendation
from Mixture-of-Modality Feedback
J. Wang, F. Yuan# , M. Cheng, etc. 首次提出采用混合模态用户反馈实现推荐系统预训练与可迁移性。 Preprint. Codes (Soon) |
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Breaking the Length Barrier: LLM-Enhanced CTR Prediction in
Long Textual User Behaviors
B. Geng, Z. Huang, X. Zhang, Y. He, L. Zhang, F. Yuan , J. Zhou, L. Ma. SIGIR(short)2024 Codes (Soon) |
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Multi-Modality is All You Need for Transferable Recommender Systems
Y. Li*, H, Du, Y Ni, P. Zhao, Q. Guo#, F. Yuan# , X. Zhou, etc. 多模态推荐系统迁移学习研究 ICDE2024 Codes |
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Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
J. Fu, F. Yuan# , Y. Song, Z. Yuan, M. Cheng, etc. 系统研究Adapter对于推荐系统迁移学习的影响 WSDM2024 Codes |
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Where to Go Next for Recommender Systems? ID- vs.Modality-based recommender models revisited
Z. Yuan*, F. Yuan* (co-first authors) , Y. Song, etc. 推荐系统何去何从，ID是否有望继续主导推荐系统社区？ SIGIR2023 (CORE rank A*) Codes |
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Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
G. Yuan*, F. Yuan* (co-first authors) , B. Kong, etc. 一个大规模的推荐系统数据集,覆盖10个任务。 NeurlPS2022 (CORE rank A*) Codes |
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Ultra-Accurate Classification and Discovery of Functional Protein-Coding Genes from Microbiomes Using FunGeneTyper: An Expandable Deep Learning-Based Framework
G. Zhang*, H. Wang* ,..., F. Yuan# , F. Ju# preprint Codes (soon) |
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Exploring evolution-based & -free protein language models as protein function predictors
M. Hu*, F. Yuan* (co-first authors) , K. Yang, etc. 首次探究AlphaFold's Evoformer蛋白质功能预测能力。 NeurlPS2022 (CORE rank A*) Codes |
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Protein Language Model Predicts Mutation Pathogenicity and Clinical Prognosis
X Liu, X Yang, L Ouyang, G Guo, J Su, R Xi*, K Yuan*, F. Yuan# 首次发现蛋白质语言模型具有预测基因突变致癌性和临床生存期。 NeurlPS2022 LMRL workshop Codes (soon) |
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Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
L. Chen, F. Yuan , J. Yang, M. Yang, C. Li Preprint. Codes (Soon) |
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Enhancing Top-N Item Recommendations by Peer Collaboration
Y. Sun*, F. Yuan* (co-first authors), M. Yang, A. Karatzoglou, L.Shen, x. Zhao SIGIR2022 (short). Codes |
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User-specific Adaptive Fine-tuning for Cross-domain Recommendations
L. Chen*, F. Yuan* (co-first authors), J. Yang, X. He, C. Li, M. Yang TKDE 2021 (CORE rank A*). Codes (Soon) |
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CmnRec: Sequential Recommendations with Chunk-accelerated Memory Network
S. Qu*, F. Yuan* (co-first authors), G. Guo, L. Zhang, W. Wei TKDE 2021 (CORE rank A*). Codes |
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One Person, One Model, One World: Learning Continual User Representation without Forgetting
F. Yuan, G. Zhang, A. Karatzoglou, J. Jose, etc. 首次提出通用用户表征终生学习问题。 SIGIR 2021 (Accept rate: 21%) (CORE rank A*). Codes Slide |
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StackRec: Efficient Training of Very Deep Sequential Recommender Models by Layer Stacking
J. Wang*, F. Yuan* (co-first authors), J. Chen, Q. Wu, C. Li, M. Yang, Y. Sun, G. Zhang SIGIR 2021 (Accept rate: 21%) (CORE rank A*). Codes |
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Learning Recommender Systems with Implicit Feedback via Soft Target Enhancement
M. Cheng*, F. Yuan* (co-first authors), Q. Liu, S. Ge, Z. Li, R. Yu, D. Lian, S. Yuan, En, Chen SIGIR 2021 (Accept rate: 21%) (CORE rank A*). Codes (Soon) |
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Iterative Pruning with Adaptive Regularization for Lifelong Sentiment Classification
B. Geng, M, Yang, F. Yuan , S. Wang, X. Ao, R. Xu SIGIR 2021 (Accept rate: 21%) (CORE rank A*). Codes |
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Learning Transferable User Representations with Sequential Behaviors via Contrastive Pre-training
M. Cheng*, F. Yuan* (co-first authors), Q. Liu, S. Ge, X. Xin, En, Chen ICDM 2021 (Accept rate: 9.9%) (CORE rank A*). Codes (Soon) |
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SkipRec: A User-Adaptive Layer Selection Framework for Very Deep Sequential Recommender Models
L. Chen*, F. Yuan* (co-first authors), M. Yang, etc. 首次发现推荐系统可以加深到100层。 AAAI 2021 (Accept rate: 21%) (CORE rank A*). Codes Slide |
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Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking
B. Geng, F. Yuan , Q. Xu, Y. Shen, R. Xu, M, Yang, ACL 2021 (Short Paper) (CORE rank A*). Codes |
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Parameter-Efficient Transfer from Sequential Behaviors for User Modeling and Recommendation
F. Yuan, X. He, A. Karatzoglou, etc. 首次发现自监督学习可以学到通用用户表征. SIGIR 2020 (Accept rate: 26%) (CORE rank A*). Codes Slide post |
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A Generic Network Compression Framework for Sequential Recommender Systems
Y. Sun*, F. Yuan* (co-first authors), M. Yang, G. Wei, Z. Zhao, D, Liu SIGIR 2020 (Accept rate: 26%) (CORE rank A*). Codes post |
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Future Data Helps Training: Modelling Future Contexts for Session-based Recommendation
F. Yuan, X. He, H.Jiang, G. Guo, J. Xiong, Z. Xu, Y. Xiong WWW 2020 (Accept rate: 19%) (CORE rank A*). Codes Slide post |
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VSE-fs: Fast Full-Sample Visual Semantic Embedding
S. Zhai, G. Guo, F. Yuan,Y. Liu, X. Wang IEEE Intelligent Systems (CORE rank A). |
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A Simple Convolutional Generative Network for Next Item Recommendation
F. Yuan, A. Karatzoglou, I. Arapakis, J. Jose, X. He. 首次应用空洞卷积于推荐系统。 WSDM 2019 (Accept rate: 16%) (CORE rank A*). Codes Slide post |
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Dynamic Item Block and Prediction Enhancing Block for Sequential Recommendation
G. Guo, S. Ouyang, X. He, F. Yuan, X. Liu IJCAI 2019.(Accept rate: 17.9%)(CORE rank A*). Codes post |
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Modeling Embedding Dimension Correlations via
Convolutional Neural Collaborative Filtering
X. Du, X. He, F. Yuan, J. Tang, Z. Qin, T. Chua TOIS 2019 (CORE rank A) Codes |
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Adversarial Training Towards Robust Multimedia Recommender System
J. Tang, X. He, X. Du, F. Yuan, Q. Tian, T. Chua TKDE 2019 (CORE rank A*). Codes |
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fBGD: Learning Embeddings From Positive Unlabeled Data with BGD
F. Yuan,X. Xin, X. He, G. Guo, W.Zhang, T. Chua, J. Jose UAI 2018. (Accept rate: 30%)(CORE rank A*). Codes post |
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Batch IS NOT Heavy: Learning Word Representations From All Samples
X. Xin*, F. Yuan* (co-first authors), X. He, J. Jose ACL 2018.(Accept rate: 24.8%)(CORE rank A*). Codes |
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Approximating Word Ranking and Negative Sampling for Word Embedding
G. Guo*, SC.Ouyang*,F. Yuan* (co-first authors) IJCAI 2018.(Accept rate: 20.4%)(CORE rank A*). Codes |
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Improving Negative Sampling for Word Representation using Self-embedded Features
L. Chen*, F. Yuan*(co-first authors), J. Jose, W.Zhang WSDM 2018.(Accept rate: 16%)(CORE rank A*). |
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BoostFM: Boosted Factorization Machines for top-N Feature-based Recommendation
F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W.Zhang ACM IUI 2017(Accept rate: 23%)(CORE rank A). |
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A Semantic Graph-Based Approach for Mining Common Topics From Multiple Asynchronous Text Streams
L. Chen, J. Jose, H. Yu, F.Yuan WWW 2017. (Accept rate: 17%)(CORE rank A*). |
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LTRo: Learning to Route Queries in Clustered P2P IR
R. Alkhawaldeh, J. Jose, Deepak P, F.Yuan ECIR 2017 (short paper)(CORE rank B). |
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A Concise Integer Linear Programming Formulation for Implicit Search Result Diversification
H. Yu, A. Jatowt, R. Blanco, H. Joho, J. Jose, L. Chen, F.Yuan WSDM 2017. (Accept rate: 15.8%)(CORE rank A*). |
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LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W.Zhang CIKM 2016. (Accept rate: 17.6%) (CORE rank A) Code post Applied in Tencent (腾讯) Recommender Systems. |
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Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation
F. Yuan, J. Jose, G. Guo, L. Chen, H. Yu, R. Alkhawaldeh ICTAI 2016. (CORE rank B) (Best Student Paper Award) |
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Optimizing Factorization Machines for Top-N Context-aware Recommendations
F. Yuan, G. Guo, J. Jose, L. Chen, H. Yu, W.Zhang WISE 2016 (CORE rank B). |
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A Semantic Graph based Topic Model for Question Retrieval in Community Question Answering
L. Chen, J. Jose, H. Yu, F.Yuan, D.Zhang WSDM 2016. (Accept rate: 18.2%) (CORE rank A*) |
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Probabilistic Topic Modelling with Semantic Graph
L. Chen, J. Jose, H. Yu, F.Yuan ECIR 2016. (Accept rate: 21%) (CORE rank B) |
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A New Strategy of Storage and Retrieval for Massive Remote Sensing Data Based on Embedded Database Files
F. Yuan, W. Gao, X.Huang, F.Huang, T.Yu, Y.Zhu International Journal of Advancements in Computing Technology 2012. |