Scholar
Yang Song
Google Scholar ID: tvWB_yUAAAAJ
Google Research - Machine Intelligence
Citations & Impact
Citations
13,721
H-index
51
i10-index
329
Publications
20
Co-authors
30
list available
Contact
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- Actively involved in organizing academic conferences and has served as Program Chairs and Area Chairs for conferences such as Recsys, WSDM, TheWebConf (WWW), CIKM, AAAI, ACM-Multimedia, and IEEE Big Data. Published multiple papers including book chapters 'Query Suggestions' (2020) and 'Using Machine Learning and Probabilistic Frameworks to Enhance Incident and Problem Management: Automated ticket classification and structuring' (2015), and a journal article 'Pone-GNN: Integrating Positive and Negative Feedback in Graph Neural Networks for Recommender Systems' in ACM Transactions on Recommender Systems (2024).
Research Experience
- - Head of Recommendation and User Growth, TikTok, California, U.S.A.
- - VP of Engineering, Kuaishou Technology, Beijing, China
- - Research Manager, Google Research/AI, Seattle/Mountain View, U.S.A.
- - Researcher, Microsoft Research (MSR), Redmond, U.S.A.
Education
- Ph.D. in Computer Science and Engineering from The Pennsylvania State University; B.S. in Computer Science and Technology from Zhejiang University, China.
Background
- Dr. Yang Song is currently the Head of Recommendation and User Growth at TikTok, managing a team of ~400 researchers and engineers to optimize TikTok's core recommender systems. His research interests include a broad range of machine learning-related fields such as information retrieval, search engine ranking, recommender systems, natural language processing, etc.
Miscellany
- Invited talks at Tsinghua University, University of Minnesota, Google Summer Camp, ACM MinneWIC 2019, Allen Institute for Artificial Intelligence (AI2), and other venues.
