Dr. Tao Qin joined Microsoft Research Asia in July 2008. His research interests include game theory (with applications to cloud computing, online and mobile advertising, ecommerce), machine learning, information retrieval and computational advertising, data mining, optimization. Prior to joining Microsoft, he got his PhD degree (2008) and Bachelor degree (2003) from Tsinghua University. He is a member of ACM and IEEE.
- We organized a workshop in KDD 2012: The Sixth International Workshop on Data Mining for Online Advertising and Internet Economy.
- We organized a workshop in SIGIR 2011: Internet Advertising.
- We organized a workshop in NIPS 2010: Machine Learning in Online Advertising.
- Microsoft Learning to Rank Datasets with tens of thousands of queries and millions of documents have been released. If you find any problems or have any suggestions, please let us know.
- LETOR: the first public learning to rank data collection. Reference paper & Bibtex
- Weidong Ma, Tao Qin, and Tie-Yan Liu, Generalized Second Price Auctions with Value Externalities, AAMAS 2014.
- Weihao Kong, Jian Li, Tao Qin, and Tie-Yan Liu, Optimal Allocation for Chunked-Reward Advertising, WINE 2013.
- Min Xu, Tao Qin, and Tie-Yan Liu, Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising, NIPS 2013.
- Wenkui Ding, Tao Qin, Xu-Dong Zhang and Tie-Yan Liu, Multi-Armed Bandit with Budget Constraint and Variable Costs, AAAI 2013.
- Weihao Kong, Jian Li, Tao Qin, and Tie-Yan Liu, Revenue Optimization for Group-Buying Websites, arXiv preprint arXiv:1305.5946.
- Wenkui Ding, Tao Wu, Tao Qin, and Tie-Yan Liu, Price of Anarchy for Generalized Second Price Auction, arXiv preprint arXiv:1305.5404.
- Xiubo Geng, Tao Qin, Xue-Qi Cheng and Tie-Yan Liu, A Noise-Tolerant Graphical Model for Ranking, Information Processing and Management, 2012.
- Sungchul Kim, Tao Qin, Hwanjo Yu and Tie-Yan Liu, An Advertiser-Centric Approach to Understand User Click Behavior in Sponsored Search, CIKM 2011.
- Xiubo Geng, Tie-Yan Liu, Tao Qin, Xue-Qi Cheng and Hang Li, Selecting Optimal Training Data for Learning to Rank, Information Processing and Management, 2011.
- Tao Qin, Xiu-Bo Geng and Tie-Yan Liu, A New Probabilistic Model for Rank Aggregation, NIPS 2010.
- Wenkui Ding, Tao Qin and Xu-Dong Zhang, Learning to Rank with Supplementary Data, AIRS 2010.
- Yajuan Duan, Long Jiang, Tao Qin, Ming Zhou and Harry Shum. An Empirical Study on Learning to Rank of Tweets, COLING 2010.
- Jiang Bian, Tie-Yan Liu, Tao Qin, Hongyuan Zha. Ranking with Query-Dependent Loss for Web Search, WSDM 2010.
- Tao Qin, Tie-Yan Liu, and Hang Li, A General Approximation Framework for Direct Optimization of Information Retrieval Measures, Information Retrieval Journal, 2009. [Technique report]
- Tao Qin, Tie-Yan Liu, Jun Xu, and Hang Li, LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval, Information Retrieval Journal, 2009. [pdf]
- Zhengya Sun, Tao Qin, Jue Wang, Qing Tao. Robust Sparse Rank Learning for Non-Smooth Ranking Measures, SIGIR 2009.
- Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Hang Li. Global Ranking Using Continuous Conditional Random Fields, NIPS 2008. [Oral Paper] [Technique report][bibtex]
- Yan-Yan Lan, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, Hang Li. Query-Level Stability and Generalization in Learning to Rank, ICML 2008.
- Xiu-Bo Geng, Tie-Yan Liu, Tao Qin, Andrew Arnold, Hang Li, Heung-Yeung Shum. Query Dependent Ranking Using K-Nearest Neighbor, SIGIR 2008.
- Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang, Wenying Xiong, Hang Li. Learning to Rank Relational Objects and Its Application to Web Search, WWW 2008. [slides]
- Tao Qin, Xu-Dong Zhang, Ming-Feng Tsai, De-Sheng Wang, Tie-Yan Liu, and Hang Li. Query-level Loss Functions for Information Retrieval. Information Processing and Management, 2008. [DOI]
- Tie-Yan Liu, Jun Xu, Tao Qin, Wenying Xiong, Hang Li. LETOR: Benchmarking "Learning to Rank for Information Retrieval", SIGIR 2007 workshop: Learning to Rank for Information Retrieval.
- Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Hang Li. Learning to Rank: From Pairwise Approach to Listwise Approach, ICML 2007.
- Tao Qin, Tie-Yan Liu, Wei Lai, Xu-Dong Zhang, De-Sheng Wang, Hang Li. Ranking with Multiple Hyperplanes, SIGIR 2007.
- Xiubo Geng, Tie-Yan Liu, Tao Qin, Hang Li. Feature Selection for Ranking, SIGIR 2007.
- Mingfeng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, Wei-Ying Ma. FRank: A Ranking Method with Fidelity Loss, SIGIR 2007.
- Yu-Ting Liu, Tie-Yan Liu, Tao Qin, Zhi-Ming Ma, Hang Li. Supervised Rank Aggregation, WWW 2007.
- Bin Gao, Tie-Yan Liu, Tao Qin, Xin Zheng, Qian-Sheng Cheng, Wei-Ying Ma. Web Image Clustering by Consistent Utilization of Low-level Features and Surrounding Texts, ACM Multimedia 2005.
- Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, Zheng Chen, Wei-Ying Ma. A Study of Relevance Propagation for Web Search, SIGIR 2005.
Area Chair, SIGIR 2013
PC member, SDM 2013, EC 2013, Big Data 2013, AIRS 2013
Co-Chair, KDD 2012 Workshop: ADKDD 2012.
PC member, CIKM 2012, ACML 2012, ADMA 2012, AIRS 2012
Area Chair, SIGIR 2012
Co-Chair, SIGIR 2011 Workshop: Internet Advertising.
PC member, WWW 2011, SIGIR 2011, CIKM 2011, ADMA 2011
Co-Chair, NIPS 2010 Workshop: Machine Learning in Online Advertising
PC member, SIGIR 2009/2010, SIGMAP 2009, EMNLP 2008