Liangwei Yang

Liangwei Yang

Research Scientist at Salesforce Research

Salesforce Research

Biography

Glad to meet you here. Currently I am a research scientist at Salesforce Research working on efficient modeling and agents. Previously I obtained my Ph.D. degree from University of Illinois Chicago, under the supervision of professor Philip S. Yu. Even before, I accomplish my Master degree under the supervision of professor Hui Gao and Tao Zhou from University of Electronic Science and Technology of China. I am always open to learn new things, and enjoy the procedure of every task. If you are an interesting person, and enjoy your life. Welcome to contact me~

Interests
  • Recommender System
  • Graph Neural Network
  • Large Language Model
Education

  • Ph.D in Computer Science, 2024

    University of Illinois at Chicago

  • M.S. in Computer Science, 2020

    University of Electronic Science and Technology of China (UESTC)

  • B.S. in Electronic Science and Technology, 2017

    University of Electronic Science and Technology of China (UESTC)

Latest News

 
 
 
 
 
Paper accepted by NeuroComputing
November 2024
Our paper Diversified Recommendation with Weighted Hypergraph Embedding: Case Study in Music has been accepted by NeuroComputing ! This is a study on diversifying recommendation with hypergraph.
 
 
 
 
 
Completed my PhD Defense
October 2024
Successfully defended my PhD thesis!
 
 
 
 
 
Paper accepted to NeuroAI@NIPS
October 2024
Our paper Beyond Directed Acyclic Computation Graph with Cyclic Neural Network has been accepted by NeuroAI@NIPS workshop. This paper finds an exciting finding to organize neural networks with cycles.

Recent Publications

Quickly discover relevant content by filtering publications.
Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu (2024). Collaborative Alignment for Recommendation. In CIKM 2024.

PDF Cite

Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zhou, Ke Xu, Liancheng Fang, Philip S. Yu (2024). Do We Really Need Graph Convolution During Training? Light Post-Training Graph-ODE for Efficient Recommendation. In CIKM 2024.

PDF Cite

Chen Wang, Ziwei Fan, Liangwei Yang, Mingdai Yang, Xiaolong Liu, Zhiwei Liu, Philip S. Yu (2024). Pre-Training with Transferable Attention for Addressing Market Shifts in Cross-Market Sequential Recommendation. In KDD 2024.

PDF Cite

Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu (2024). Instruction-based Hypergraph Pretraining. In SIGIR 2024.

PDF Cite

Yu Wang, Zhiwei Liu, Liangwei Yang, Philip S. Yu (2024). Conditional denoising diffusion for sequential recommendation. In PAKDD 2024.

PDF Cite

Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang, Chen Wang, Hao Peng, Philip S. Yu (2024). Knowledge Graph Context-Enhanced Diversified Recommendation. In WSDM 2024.

PDF Cite

Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu (2024). Unified Pretraining for Recommendation via Task Hypergraphsn. In WSDM 2024.

PDF Cite

See all publications

Experience

 
 
 
 
 
Research Scientist
Salesforce Research
Jan 2024
Palo Alto
 
 
 
 
 
Research Intern
Salesforce Research
May 2023 – Nov 2023
Palo Alto
 
 
 
 
 
Research Intern
ByteDance Applied Machine Learning Group
May 2022 – Aug 2022
Chicago

Contact