About Me

I have joined Kuaishou(HK.1024) as the Head of Recommendation (VP of Engineering) since June 2020, overseeing both core modeling and data mining teams. I manage and lead 100+ engineers and scientists to develop machine learning models that power Kuaishou's recommendation and ads engines in all major services, including domestic products (Kuaishou and Kuaishou Express) and international products (Kwai). We are one of the world's leading short-video content communities and social platforms with 1 billion monthly active users, and still growing at a tremendous speed (Bloomberg, Forbes). I'm also actively oraganizing academic conferences and have served as PC&Area Chairs in Recsys, WSDM, TheWebConf(WWW), IEEE Big Data etc.

I was a Research&Engineering Manager in the Deep Dialogue group of Machine Intelligence in Google AI, working on building the world's best dialogue engines for Google products using deep learning, NLP and recommender systems. Before that, I was a Senior Software Engineer at Google Seattle working on user modeling and machine learning for Ads.

I spent over 7 years at Microsoft Research before joining Google. I was a Researcher at the Deep Learning Technology Center at Microsoft Research Redmond. Before that, I was a Researcher affiliated with Internet Services Research Center (ISRC),  Search Quality & Cyber-Intelligene Lab (SQ-CIL) in MSR Redmond. I joined MSR in 2009 after I got my Ph.D. During my Ph.D, I worked with my advisor C. Lee Giles on the next generation scientific literature search engine CiteSeer, which was once the world most popular search engines for scientific literature [screenshot].

Here is the link to my Google Scholar page.

[new]The first comprehensive book about query understanding for search engines has been published. We co-authored the chapter of query suggestions.

We have recently open-sourced our code that implemented Multi-view DNN and Temporal DSSM in Keras [github link].

Employment

2020~now, VP of Engineering, Kuaishou Technology, Beijing, China.
2016~2020, Research Manager, Google Research/AI, Seattle/Mountain View, U.S.A.
2009~2016, Researcher, Microsoft Research (MSR), Redmond, U.S.A.
2008 Summer, Research Intern, IBM TJ Watson, Hawthorne, NY, U.S.A.
2007 Summer, Research Intern, Microsoft Research, Redmond, WA, U.S.A.
2005 Summer, Research Intern, Ask.com, Piscataway, NJ. U.S.A.

Education

Ph.D. in Computer Science and Engineering,  The Pennsylvania State University
B.S. in Computer Science and Technology, Zhejiang University, China

Research Interests

My research includes a broad interests of machine learning-related fields, e.g., information retrieval, search engine ranking, recommender systems, natural language processing and so on.

Invited Talks & Media Coverages

• [new]1.9 Trillion Parameters: Kuaishou productized world's largest recommendation model (In Chinese: toutiao, jiqizhixin)
• 10x Thinking and More: How Google Works, Univeristy of Minnesota, April, 2020.
• Recent Advance in Dialogue Systems and NLP Research, Google Summer Camp, Jan, 2020.
• Google Culture and Beyond, ACM MinneWIC 2019, Feb, 2019.
• Semantic Search and Proactive Discovery, Allen Institute for Artificial Intelligence (AI2), Feb, 2015.
• Keynote talk on Knowledge-powered Next Generation Scholarly Search and Recommendation Engines. AAAI 2015 Workshop on Scholarly Big Data: AI Perspectives, Challenges, and Ideas. Jan, 2015.
• Bing Dialog Model: Intent, Knowledge and User Interaction. Penn State University. March, 2013.

Publications

Book Chapters

• Zhen Liao, Yang Song and Denny Zhou, Query Suggestions, in the Book of Query Understanding for Search Engines, 2020.
• Anca Sailer, Ruchi Mahindru, Yang Song and Xing Wei, Using Machine Learning and Probabilistic Frameworks to Enhance Incident and Problem Management: Automated ticket classification and structuring, in the Book of  Maximizing Management Performance and Quality with Service Analytics, 2015.

Referred Conference Proceedings

(*) are students I mentored for their summer internships at Microsoft Research and Google Research.

2024

• Teng Shi, Zihua Si, Jun Xu, Xiao Zhang, Xiaoxue Zang, Kai Zheng, Dewei Leng, Yanan Niu and Yang Song, UniSAR: Modeling User Transition Behaviors between Search and Recommendation, in SIGIR'24 (Full Paper).
• Zhongxiang Sun, Zihua Si, Xiao Zhang, Xiaoxue Zang, Yang Song, Hongteng Xu and Jun Xu, To Search or to Recommend: Predicting Open-App Motivation with Neural Hawkes Process, in SIGIR'24 (Full Paper).
• Kai Zheng, Haijun Zhao, Rui Huang, Beichuan Zhang, Na Mou, Yanan Niu, Yang Song, Hongning Wang and Kun Gai, Full Stage Learning to Rank: A Unified Framework for Multi-Stage Systems, TheWebConf'24 (Research Track).
• Guanyu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang Song, Kun Gai, Zhiheng Li, Depeng Jin, Yong Li and Meng Wang, Mixed Attention Network for Cross-domain Sequential Recommendation, in WSDM'24 (Oral).
• Guanyu Lin, Yu Zheng, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Zhiheng Li, Depeng Jin and Yong Li, Inverse Learning with Extremely Sparse Feedback for Recommendation, in WSDM'24 (Oral).
• Yimeng Bai, Yang Zhang, Jing Lu, Jianxin Chang, Xiaoxue Zang, Yanan Niu, Yang Song and Fuli Feng, LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting, in WSDM'24.
• Ziyang Liu, Chaokun Wang, Liqun Yang, Yunkai Lou, Hao Feng, Cheng Wu, Kai Zheng and Yang Song, Incorporating Dynamic Temperature Estimation into Contrastive Learning on Graphs, in ICDE'24.

2023

• Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang Song and Kun Gai, Graph Contrastive Learning with Generative Adversarial Network, in KDD 2023.
• Jianxin Chang, Chenbin Zhang, Zhiyi Fu, Xiaoxue Zang, Lin Guan, Jing Lu, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song and Kun Gai, TWIN: TWo-stage Interest Network for Lifelong User Behavior Modeling in CTR Prediction at Kuaishou, in KDD 2023.
• Jianxin Chang, Chenbin Zhang, Yiqun Hui, Dewei Leng, Yanan Niu, Yang Song and Kun Gai, PEPNet: Parameter and Embedding Personalized Network for Infusing with Personalized Prior Information, in KDD 2023.
• Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou and Kun Gai, Multi-behavior Self-supervised Learning for Recommendation, in SIGIR 2023 (Full Research Paper).
• Zihua Si, Zhongxiang Sun, Xiao Zhang, Jun Xu, Xiaoxue Zang, Yang Song, Kun Gai and Ji-Rong Wen, When Search Meets Recommendation: Learning Disentangled Search Representation for Recommendation, in SIGIR 2023 (Full Research Paper).
• Guanyu Lin, Chen Gao, Yu Zheng, Jianxin Chang, Yanan Niu, Yang Song, Zhiheng Li, Depeng Jin and Yong Li, Dual-interest Factorization-heads Attention for Sequential Recommendation, in TheWebConf(WWW) 2023.
• Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang Song, Kai Zheng, Xiaowei Wang, and Guorui Zhou, Instant Representation Learning for Recommendation over Large Dynamic Graphs, in ICDE 2023.
• Yunzhu Pan, Chen Gao, Yang Song, Kun Gai, Depeng Jin and Yong Li, Towards the Understanding and Modeling of Passive-Negative Feedback in Sequential Short-video Recommendation, in RecSys 2023.
• Yang Zhang, Yimeng Bai, Jianxin Chang, Xiaoxue Zang, Song Lu, Jing Lu, Fuli Feng, Yanan Niu and Yang Song, Leveraging Watch-time Feedback for Short-Video Recommendations: A Causal Labeling Framework, in CIKM' 23.
• Beichuan Zhang, Chenggen Sun, Jianchao Tan, Xinjun Cai, Jun Zhao, Mengqi Miao, Kang Yin, Chengru Song, Na Mou and Yang Song, SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems, in CIKM' 23.
• Yunzhu Pan, Nian Li, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Depeng Jin and Yong Li, Learning and Optimization of Implicit Negative Feedback for Industrial Short-video Recommender System, in CIKM' 23.
• Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Dewei Leng, Yanan Niu, Yang Song, Xiao Zhang and Jun Xu, KuaiSAR: A Unified Search And Recommendation Dataset, in CIKM' 23 (Resource Track).

2022

• Yu Tian, Jianxin Chang, Yanan Niu, Yang Song and Chenliang Li. When Multi-Level Meets Multi-Interest: A Multi-Grained Neural Model for Sequential Recommendation, in SIGIR 2022.
• Zihua Si, Xueran Han, Xiao Zhang, Jun Xu, Yue Yin, Yang Song and Ji-Rong Wen, A Model-Agnostic Causal Learning Framework for Recommendation using Search Data, in TheWebConf(WWW) 2022.
• Yu Zheng, Chen Gao, Jianxin Chang, Yanan Niu, Yang Song, Depeng Jin and Yong Li, Disentangling Long and Short-Term Interests for Recommendation, in TheWebConf(WWW) 2022.
• Tiankai Gu, Chaokun Wang, Cheng Wu, Yunkai Lou, Jingcao Xu, Changping Wang, Kai Xu, Can Ye and Yang Song, HybridGNN: Learning Hybrid Representation for recommendation in Multiplex Heterogeneous Networks, in ICDE 2022.
• Kunpeng Li, Guangcui Shao, Naijun Yang, Xiao Fang and Yang Song, Billion-user Customer Lifetime Value Prediction: An Industrial-scale Solution from Kuaishou, in CIKM 2022 Applied Science Track.

2021

• Jianxin Chang, Chen Gao, Yu Zheng, Yiqun Hui, Yanan Niu, Yang Song, Depeng Jin and Yong Li, Sequential Recommendation with Graph Convolutional Networks, in SIGIR 2021.
• Xiaoxue Zang, Lijuan Liu, Maria Wang, Yang Song, Hao Zhang and Jindong Chen, PhotoChat: A Human-Human Dialogue Dataset With Photo Sharing Behavior For Joint Image-Text Modeling, in ACL 2021 (main conference).
• Yiyu Liu, Qian Liu, Yu Tian, Changping Wang, Yanan Niu, Yang Song and Chenliang Li, Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation>, in CIKM 2021.
• Xinying Song, Alex Salcianu, Yang Song, Dave Dopson and Denny Zhou, Fast WordPiece Tokenization, in EMNLP 2021 (main conference).
• Sanqiang Zhao*, Raghav Gupta, Yang Song and Denny Zhou, Extremely Small BERT Models from Mixed-Vocabulary Training, in EACL 2021.

2016

• Qi Guo and Yang Song (equal contribution), Large-Scale Analysis of Viewing Behavior: Towards Measuring Satisfaction with Mobile Proactive Systems, in CIKM 2016 , ACM – Association for Computing Machinery, October 2016.
• Yang Song, Ali Elkahky*, and Xiaodong He, Multi-Rate Deep Learning for Temporal Recommendation, in SIGIR 2016 , ACM – Association for Computing Machinery, July 2016 (short paper). [code]
• Yang Song and Qi Guo, Query-Less: Predicting Task Repetition for NextGen Proactive Search and Recommendation Engines, in WWW 2016 , WWW – World Wide Web Consortium (W3C), April 2016
• Liu Yang^*, Qi Guo, Yang Song, Sha Meng, Milad Shokouhi, Kieran McDonald, and W. Bruce Croft, Modelling User Interest for Zero-query Ranking, in European Conference on Information Retrieval ( ECIR 2016), March 2016.
• Zhaohui Wu^*, Yang Song, and C. Lee Giles, Exploring Multiple Feature Spaces for Novel Entity Discovery, in AAAI 2016 , AAAI - Association for the Advancement of Artificial Intelligence, February 2016.

2015

• Ali Mamdouh Elkahky^*, Yang Song, and Xiaodong He, A Multi-View Deep Learning Approach for User Modeling in Recommendation Systems, in WWW 2015 , May 2015. [code]
• Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, and Kuansan Wang, An Overview of Microsoft Academic Service (MAS) and Applications, WWW– World Wide Web Consortium (W3C), 18 May 2015.
• Chieh-Han Wu and Yang Song, Robust and Distributed Web-Scale Near-Dup Document Conflation in Microsoft Academic Service, in IEEE International Conference on Big Data - Workshop on Data Quality Issues , IEEE – Institute of Electrical and Electronics Engineers, October 2015.

2014

• Yang Song, Xiaolin Shi, Ryen White, and Ahmed Hassan, Context-Aware Web Search Abandonment Prediction, in ACM SIGIR 2014 , ACM, July 2014
• Hongning Wang^*, Yang Song, Ming-Wei Chang, Xiaodong He, Ahmed Hassan, and Ryen White, Modeling Action-level Satisfaction for Search Task Satisfaction Prediction, in ACM SIGIR 2014 , ACM, July 2014
• Yang Song, Hongning Wang*, and Xiaodong He, Adapting Deep RankNet for Personalized Search, in WSDM 2014 , ACM, February 2014
• Yang Song, Weiwei Cui, Shixia Liu, and Kuansan Wang, Online Behavioral Genome Sequencing from Usage Logs: Decoding the Search Behaviors, in WWW 2014 , ACM, March 2014 (Demo)

2013

• Hongning Wang*, Xiaodong He, Ming-Wei Chang, Yang Song, Ryen White, and Wei Chu, Personalized Ranking Model Adaptation for Web Search, in The 36th Annual ACM SIGIR Conference ( SIGIR'2013), ACM, July 2013
• Yang Song, Xiaolin Shi, and Xin Fu, Evaluating and Predicting User Engagement Change with Degraded Search Relevance , in WWW 2013 , ACM, May 2013
• Yang Song, Hao Ma, Hongning Wang *, and Kuansan Wang, Exploring and Exploiting User Search Behavior on Mobile and Tablet Devices to Improve Search Relevance, in WWW 2013 , ACM, May 2013
• Ryen White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song, and Hongning Wang, Enhancing Personalized Search by Mining and Modeling Task Behavior, in WWW 2013 , ACM, 2013
• Hongning Wang *, Yang Song, Ming-Wei Chang, Xiaodong He, Ryen White, and Wei Chu, Learning to Extract Cross-Session Search Tasks, in WWW 2013 , ACM, 2013

2012

• Zhen Liao *, Yang Song, Li-wei He, and Yalou Huang, Evaluating the Effectiveness of Search Task Trails, in WWW 2012 , ACM, April 2012
• Yang Song, Dengyong Zhou, and Li-wei He, Query Suggestion by Constructing Term-Transition Graphs, in WSDM '12 , ACM, 8 February 2012

2011

• Yang Song, Umer Farooq, and Baojun Qiu, Hierarchical Tag Visualization and Application for Tag Recommendations, in In Proceedings of the 20th ACM international conference on Information and knowledge management ( CIKM '11), Association for Computing Machinery, Inc., 24 October 2011
• Ahmed Hassan *, Yang Song, and Li-wei He, A Task Level User Satisfaction Metric and its Application on Improving Relevance Estimation, in ACM Conference on Information and Knowledge Management ( CIKM), Association for Computing Machinery, Inc., 1 October 2011
• Yang Song, Dengyong Zhou, and Li-wei He, Post-Ranking Query Suggestion by Diversifying Search Results, in SIGIR '11 Proceedings of the 34st annual international ACM SIGIR conference on Research and development in information retrieval, Association for Computing Machinery, Inc., July 2011
• Yang Song, Nam Nguyen *, Li-wei He, Scott Imig, and Robert Rounthwaite, Searchable Web Sites Recommendation, in WSDM'11: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Association for Computing Machinery, Inc., 2011

2010

• Yang Song and Li-wei He, Optimal Rare Query Suggestion With Implicit User Feedback, in WWW '10 Proceedings of the 19th international conference on World wide web, Association for Computing Machinery, Inc., 2010
• Kamal Jain, Yang Song, Li-wei He, and Mary Czerwinski, Evaluating the Unaccounted Cost of Distraction of Display Ads to the Users, in Web Science Conference 2010 ( WebSci 2010), Association for Computing Machinery, Inc., 2010

2009

• Yang Song, Anca Sailer, and HIdayatullah Shaikh, Problem Classification Method to Enhance the ITIL Incident, Problem and Change Management Process, in the 11th IFIP/IEEE International Symposium on Integrated Network Management ( IM 2009), IEEE, June 2009

2008

• Yang Songand C. Lee Giles, Efficient User Preference Predictions Using Collaborative Filtering, in the 19th International Conference on Pattern Recognition ( ICPR 2008), IEEE, December 2008
• Yang Song, Lu Zhang, and C. Lee Giles, A Non-parametric Approach to Pair-wise Dynamic Topic Correlation Detection, in IEEE International Conference on Data Mining series ( ICDM 2008), IEEE, December 2008
• Yang Song, Lu Zhang, and C. Lee Giles, Sparse Gaussian Processes Classification for Fast Tag Recommendation, in ACM 17th Conference on Information and Knowledge Management ( CIKM 2008), Association for Computing Machinery, Inc., October 2008
• Yang Song, Ziming Zhuang, Huajing Li, Qiankun Zhao, Jia Li, Wang-Chien Lee, and C. Lee Giles, Real-time Automatic Tag Recommendation, in the 31st Annual International ACM SIGIR Conference ( SIGIR 2008), Association for Computing Machinery, Inc., July 2008

2007

• Umer Farooq, Thomas G. Kannampallil, Yang Song, Crag H. Ganoe, John Carroll, and C. Lee Giles, Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics, in the 2007 international ACM Conference on Supporting Group Work ( GROUP '07), Association for Computing Machinery, Inc., November 2007
• Jian Huang, Seyda Erekia, Yang Song, Hongyuan Zha, and C. Lee Giles, Efficient Multiclass Boosting Classification with Active Learning, in Seventh SIAM International Conference ( SDM 2007), Society for Industrial and Applied Mathematics, September 2007
• Yang Song, Jian Huang, Ding Zhou, Hongyuan Zha, and C. Lee Giles, IKNN: Informative K-Nearest Neighbor Classification, in PKDD 2007 , Springer Verlag, September 2007
• Yang Song, Jian Huang, Isaac G. Councill, Jia Li, and C. Lee Giles, Efficient Topic-based Unsupervised Name Disambiguation, in the 7th ACM/IEEE-CS joint conference on Digital libraries ( JCDL 2007), Association for Computing Machinery, Inc., June 2007
• Yang Song, Jian Huang, Isaac G. Councill, Jia Li, and C. Lee Giles, Generative Models for Name Disambiguation, in the 16th international conference on World Wide Web ( WWW 2007), Association for Computing Machinery, Inc., April 2007

2006

• Yang Song, Ding Zhou, Jian Huang, Isaac G. Councill, Hongyuan Zha, and C. Lee Giles, Boosting the Feature Space: Text Categorization for Unstructured Data on the Web, in the Sixth IEEE international Conference on Data Mining, ( ICDM 2006), IEEE, December 2006

2005

• Ding Zhou, Yang Song, Ya Zhang, and Hongyuan Zha, Towards Discovering Organizational Structure from Email Corpus, in the 4th IEEE International Conference on Machine Learning and Applications, Los Angeles , CA, U.S.A. 2005 ( ICMLA 2005)., IEEE, August 2005
• Huajing Li, Isaac G. Councill, Levent Bolelli, Ding Zhou, Yang Song, Wang-Chien Lee, A. Sivasubrana, and C. Lee Giles, CiteSeerX - A scalable autonomous scientific digital library, in the First International Conference on Scalable Information Systems ( INFOSCALE 06), IEEE, August 2005

Academic Services

• Organizing Committee:
  • Industrial Program Co-chair for Recsys 2023
  • WSDM CUP 2023 Chair, WSDM 2023 Conference
  • Program Co-chair for IEEE Big Data 2018 Conference
• Program Committee of NeurIPS, ICML, AAAI, WWW, WSDM, ECML/PKDD, ACL, EMNLP, SIGIR, CIKM
• Journal Reviewer of Journal of Machine Learning Research (JMLR) , ACM Transactions on Information Systems (TOIS) , ACM Transactions on Internet Technology (TOIT) , ACM Transactions on Intelligent Systems and Technology (TIST) , Journal of Information Retrieval , Journal of Pattern Recognition , Journal of American Society for Information Science and Technology , Journal of Web Semantics , IET Information Security