基本信息
徐君 男 博导 中国科学院计算技术研究所
电子邮件： junxu@ict.ac.cn
通信地址： 北京市海淀区中关村科学院南路6号中科院计算所
邮政编码： 100190
个人介绍徐君研究员别于2001年和2006年获得南开大学信息技术科学学院学士学位和博士学位。博士毕业后，徐君先后就职于微软亚洲研究院和华为公司诺亚方舟实验室，任副研究员、研究员和高级研究员，于2014年6月加入中国科学院计算技术研究所网络数据科学与技术重点实验室，任研究员。徐君的研究兴趣集中于信息检索、机器学习和网络大数据分析，在包括SIGIR、WWW、WSDM、CIKM等顶级国际学术会议和JMLR、TOIS等顶级国际期刊发表了二十几篇高质量论文，获得授权美国专利8项、中国专利1项，并获得AIRS ’10和ICMLC ’05最佳论文奖。担任过SIGIR 2014国际研讨会Semantic Matching in Information Retrieval (SMIR ’14)共同主席，担任亚洲机器学习会议ACML 2014和ACML 2015高级程序委员会委员(Senior PC)，多次担任SIGIR、WWW、WSDM等顶级国际学术会议的程序委员会委员。


招生信息

招生专业081202


招生方向互联网搜索与挖掘

教育背景2001-09--2006-06 南开大学 博士
1997-09--2001-06 南开大学 本科

工作经历

工作简历2014-06~现在, 中国科学院计算技术研究所, 研究员
2012-09~2014-05,华为技术有限公司诺亚方舟实验室, 研究员、高级研究员
2006-07~2012-09,微软亚洲研究院, 副研究员
2001-09~2006-06,南开大学, 博士
1997-09~2001-06,南开大学, 本科

教授课程网络数据挖掘

专利与奖励

奖励信息（1）第6届亚洲信息检索学术会议(AIRS 2010)最佳论文奖,院级,2010
（2）第4届国际机器学习与控制会议(ICMLC 2005)最佳论文奖,院级,2005


专利成果（ 1 ）Directly Optimizing Evaluation Measures in Learning to Rank,发明,2013,第 1 作者,专利号: US 8,478,748 B2.
（ 2 ）Topics in Relevance Ranking Model for Web Search,发明,2011,第 2 作者,专利号: US 8,065,310 B2
（ 3 ）Search Results Ranking using Editing Distance and Document Information,发明,2014,第 4 作者,专利号: US 8,812,493 B2
（ 4 ）Ranking and Accessing Definitions of Terms,发明,2011,第 3 作者,专利号: US 7,877,383 B2
（ 5 ）Search by Document Type and Relevance,发明,2010,第 3 作者,专利号: US 7,644,074 B2
（ 6 ）Query Expansion for Web Search,发明,2014,第 1 作者,专利号: US 8,898,156 B2
（ 7 ）Regularized Latent Semantic Indexing for Topic Modeling,发明,2013,第 1 作者,专利号: US 8,533,195 B2
（ 8 ）Learning a Document Ranking Using a Loss Function with a Rank Pair or a Query Parameter,发明,2009,第 2 作者,专利号: US 7,593,934 B2

发表论文1.Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures. Proceedings of the 38th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’15), to appear.

2.Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu and Xueqi Cheng. Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures. Proceedings of the 38th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’15), to appear.

3.Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu and Xueqi Cheng. Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference of the Asian Federation of Natural Language Processing (ACL-IJCNLP ’15), to appear.

4.Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. A Probabilistic Model for Bursty Topic Discovery in Microblogs. The Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI ’15), Austin Texas, USA, pp. 353-359, 2015.

5.Fangzhao Wu, Jun Xu, Hang Li, and Xin Jiang. Ranking Optimization with Constraints. The 23rd ACM Conference on INformation and Knowledge Management (CIKM ’14), Shanghai, China, pp. 1049-1058, 2014.

6.Quan Wang, Jun Xu, and Hang Li. User Message Model: A New Approach to Scalable User Modeling on Microblog. The Tenth Asia Information Retrieval Societies Conference (AIRS ’14), Kuching, Malaysia, pp. 209-220, 2014.

7.Hang Li and Jun Xu. Semantic Matching in Search. Foundations and Trends in Information Retrieval 7(5): 343-469, Now Publishers, 2014.

8. Quan Wang, Jun Xu, Hang Li, and Nick Craswell. Regularized Latent Semantic Indexing: A New Approach to Large Scale Topic Modeling. ACM Transaction on Information System (TOIS), Volume 31, Issue 1, 2013.

9. Wei Wu, Hang Li, and Jun Xu. Learning query and document similarities from click-through bipartite graph with metadata. Proceedings of the sixth ACM international conference on Web search and data mining (WSDM ’13), Rome, Italy, pp. 687-696, 2013.

10.Quan Wang, Zheng Cao, Jun Xu, and Hang Li. Group Matrix Factorization for Scalable Topic Modeling. Proceedings of the 35th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’12), Portland, Oregon, USA, pp. 375-384, 2012.

11.Quan Wang, Jun Xu, Hang Li, and Nick Craswell. Regularized Latent Semantic Indexing. Proceedings of the 34th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’11), Beijing China, pp. 685-694, 2011.

12.Wei Wu, Jun Xu, Hang Li, and Satoshi Oyama. Learning Robust Relevance Model for Search using Kernel Method. Journal of Machine Learning Research (JMLR), 12(May):1429-1458, 2011.

13.Jun Xu, Wei Wu, Hang Li, and Gu Xu. A Kernel Approach to Addressing Term Mismatch. Proceedings of the 20th international conference companion on World Wide Web (WWW ’11), Hyderabad India, pp. 153-154, 2011.

14.Jun Xu, Hang Li, and Chaoliang Zhong. Relevance Ranking using Kernels. The 6th Asia Information Retrieval Societies Conference (AIRS ’10), Taipei, Taiwan, pp. 1-12, 2010. (Best Paper Award)

15.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, 2010.

16.Weijian Ni, Jun Xu, Hang Li, and Yalou Huang. Group-based Learning — A Boosting Approach. Proceedings of the 17th ACM Conference on Information and Knowledge Management (CIKM ’08), Napa Valley, California, pp. 1443-1444, 2008.

17. Tao Qin, Tie-Yan Liu, Jun Xu, and Hang Li. How to Make LETOR More Uwseful and Reliable. Proceedings of SIGIR 2008 Workshop on Learning to Rank for Information Retrieval (LR4IR ’08), Singapore, pp. 52-58, 2008.

18.Jun Xu, Tie-Yan Liu, Min Lu, Hang Li, and Wei-Ying Ma. Directly Optimizing Evaluation Measures in Learning to Rank. Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’08), Singapore, pp. 107-114, 2008.

19. Jun Xu and Hang Li. AdaRank: A Boosting Algorithm for Information Retrieval. Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’07), Amsterdam, The Netherlands, pp. 391-398, 2007.

20.Tie-Yan Liu, Jun Xu, Tao Qin, Wenying Xiong, and Hang Li. LETOR: Benchmarking “Learning to Rank for Information Retrieval”. Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval (LR4IR ’07), Amsterdam, The Netherlands, pp. 3-10, 2007.

21.Jun Xu, Yunbo Cao, Hang Li, Nick Craswell, and Yalou Huang. Searching Documents Based on Relevance and Type. Proceedings of the 29th European Conference on Information Retrieval (ECIR ’07), Rome, Italy, pp. 629-636, 2007.

22.刘铁岩, 徐君, 李航, 马维英. 为搜索引擎学习最优的排序模型. 中国计算机学会通讯. 第3卷, 第10期, 32-37, 2007年10月.

23.Jun Xu, Yunbo Cao, Hang Li, and Yalou Huang. Cost-sensitive Learning of SVM for Ranking. Proceedings of the 17th European Conference on Machine Learning (ECML ’06), Berlin, Germany, pp. 833-840, 2006. (pdf)

24.Yunbo Cao, Jun Xu, Tie-Yan Liu, Hang Li, Yalou Huang, and Hsiao-Wuen Hon. Adapting ranking SVM to document retrieval. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’06), Seattle, Washington, USA, pp. 186-193, 2006.

25.Jun Xu, Yunbo Cao, Hang Li, Min Zhao, and Yalou Huang. A Supervised Learning Approach to Search of Definitions. Journal of Computer Science and Technology (JCST), Vol. 21(3), pp. 439-449, 2006.

26. Jun Xu and Ya-lou Huang. Using SVM to Extract Acronyms from Text. Soft Computing - A Fusion of Foundations, Methodologies and Applications, Springer Berlin Heidelberg, Volume 11, Issue 4, pp. 369-373, 2006.

27. Hang Li, Yunbo Cao, Jun Xu, Yunhua Hu, Shenjie Li, and Dmitriy Meyerzon, A New Approach to Intranet Search Based on Information Extraction. Proceedings of the 14th ACM international conference on Information and knowledge management (CIKM ’05), industry track, Bremen, Germany, pp. 460-468, 2005.

28. Jun Xu, Yunbo Cao, Hang Li, and Min Zhao. Ranking Definitions with Supervised Learning Methods. Proceedings of the 14th International World Wide Web Conference (WWW ’05), Industrial and Practical Experience Track, Chiba, Japan, pp. 811-819, 2005.

29. Jun Xu and Ya-lou Huang. A Machine Learning Approach to Recognizing Acronyms and Their Expansions. Proceedings of the 4th International Conference on Machine Learning and Cybernetics (ICMLC ’05), Guangzhou, China, Vol. 4, pp. 2313-2319, 2005. (Best Paper Award)


发表论文（1） Text Matching as Image Recognition, Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI '16), 2016, 第 4 作者
（2） A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations, Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI '16), 2016, 第 4 作者
（3） Inside Out: Two Jointly Predictive Models for Word Representations and Phrase Representations, Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI '16), 2016, 第 4 作者
（4） SPAN: Understanding a Question with its Support Answers, Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI '16), 2016, 第 4 作者
（5） Your Cart tells You: Inferring Demographic Attributes from Purchase Data, Proceedings of the 9th ACM International Conference on Web Search and Data Mining (WSDM '16), 2016, 第 4 作者
（6） Modeling Parameter Interactions in Ranking SVM, Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM '15), 2015, 第 2 作者
（7） Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures, Proceedings of the 38th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2015), 2015, 第 2 作者
（8） Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations, Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2015), 2015, 第 4 作者
（9） Learning Hierarchical Representation Model for Next Basket Recommendation, Proceedings of the 38th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2015), 2015, 第 4 作者
（10） A Probabilistic Model for Bursty Topic Discovery in Microblogs, Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015), 2015, 第 4 作者
（11） Ranking Optimization with Constraints, Proceedings of the 23rd ACM Conference on Information and Knowledge Management (CIKM 2014), 2014, 第 2 作者
（12） User Message Model: A New Approach to Scalable User Modeling on Microblog, Proceedings of the 10th Asia Information Retrieval Societies Conference (AIRS 2014), 2014, 第 2 作者
（13） Learning query and document similarities from click-through bipartite graph with metadata, Proceedings of the sixth ACM international conference on Web search and data mining (WSDM 2013), 2013, 第 3 作者
（14） Regularized Latent Semantic Indexing: A New Approach to Large Scale Topic Modeling, ACM Transaction on Information System (TOIS), 2013, 第 2 作者
（15） Group Matrix Factorization for Scalable Topic Modeling, Proceedings of the 35th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2012), 2012, 第 3 作者
（16） Regularized Latent Semantic Indexing, Proceedings of the 34th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 2011), 2011, 第 2 作者
（17） Learning Robust Relevance Model for Search using Kernel Method, Journal of Machine Learning Research (JMLR), 2011, 第 2 作者


发表著作（ 1 ）互联网搜索中的语义匹配, Semantic Matching in Search, Now Publishers, 2013-07, 第 2 作者