徐君

徐君分别于2001年和2006年获得南开大学学士学位和博士学位。毕业后先后就职于微软亚洲研究院、华为诺亚方舟实验室和中科院计算所,任副研究员和研究员,2018年9月加入中国人民大学,任教授。徐君的研究兴趣包括信息检索、机器学习和大数据分析,在SIGIR、WWW、ACL、CIKM、WSDM、AAAI等国际学术会议和JMLR、TKDE、TOIS、TIST等期刊发表论文五十余篇,获专利授权10项;所提出的排序学习算法收录于多本国际知名信息检索教科书和计算机手册,在多所国际知名大学的课程中被讲授,应用于开源系统Lemur以及微软、华为的搜索产品;获SIGIR \'17和SIGIR \'18 Test of time award提名、CIKM \'17 Best Full Paper Runner-up、AIRS \'10和ICMLC \'05最佳论文奖。徐君是期刊JASIST编委成员,担任SIGIR、AAAI、WWW和ACML等国际会议的高级程序委员会委员(Senior PC)。

详细>>

电子邮箱:junxu@ruc.edu.cn

详细资料

教育经历

1997-2001 南开大学 本科

2001-2006 南开大学 博士

工作经历

2006年-2012年 微软亚洲研究院 副研究员

2012年-2014年 华为诺亚方舟实验室 资深研究员

2014年-2018年 中国科学院计算技术研究所 研究员、博导

2018年至今 中国人民大学教授

研究方向

信息检索,互联网搜索,机器学习,大数据分析

讲授课程

数据科学导论(本科)

智能信息检索(研究生)

对学生的培养要求

徐君教授具有丰富指导学生的经验。在微软亚洲研究院和华为诺亚方舟实验室工作的8年多时间里指导过20余名实习生;在中科院计算所工作期间共指导博士生7名、硕士生7名,学生毕业后就职于谷歌、微软、腾讯、阿里巴巴、搜狗等国内外知名企业。

学生能力培养目标:

科学素养培养:理解基本科学观点和科学探究过程,认识科学技术对人类生活工作所产生的影响;

专业能力培养:培养学生的科学研究能力(论文阅读、工作调研、问题分析、方法设计、实验分析、论文写作等)、系统研发能力(编程、系统设计、项目管理),结合学生的特长和兴趣为学生制定不同的培养计划;

欢迎各位有意向攻读硕士或博士学位的同学报考!

科研项目

1.国家自然科学基金面上项目:基于强化学习的信息检索排序模型研究;主持

2.开源系统:大数据分析系统EasyML https://github.com/ICT-BDA/EasyML

科研成果

论文:

1.Jun Xu, Wei Zeng, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Modeling the Parameter Interactions in Ranking SVM with Low-rank Approximation. IEEE Transactions on Knowledge and Data Engineering (TKDE). (accepted)

2.Jun Xu, Xiangnan He, and Hang Li. Deep Learning for Matching in Search and Recommendation. Proceedings of the 41st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR \'18), Ann Arbor, MI, USA, pp. 1365-1368, 2018. (tutorial)

3.Yue Feng, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng. From Greedy Selection to Exploratory Decision-Making: Diverse Ranking with Policy-Value Networks. Proceedings of the 41st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR \'18), Ann Arbor, MI, USA, pp. 125-134, 2018.

4.Sihao Yu, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Variance Reduction of Policy Gradient Bandit Problem via Antithetic Variates. Proceedings of the 41st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR \\\'18), Ann Arbor, MI, USA, pp. 885-888, 2018. (short paper)

5.Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, Chengxiang Zhai, and Xueqi Cheng. Modeling Diverse Relevance Patterns in Ad-hoc Retrieval. Proceedings of the 41st annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR \\\'18), Ann Arbor, MI, USA, pp. 375-384, 2018.

6.Ruqing Zhang, Jiafeng Guo, Yixing Fan, Yanyan Lan, Jun Xu, and Xueqi Cheng. Learning to Control the Specificity in Neural Response Generation. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia (ACL \\\'18), Melbourne, Australia, pp. 1108-1117, 2018.

7.Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. Tailored Sequence to Sequence Models for Different Conversation Scenarios. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL \\\'18), Melbourne, Australia, pp. 1479-1488, 2018.

8.Guoxin Cui, Jun Xu, Wei Zeng, Yanyan Lan, Jiafeng Guo and Xueqi Cheng. MQGrad: Reinforcement Learning of Gradient Quantization in Parameter Server. Proceedings of the 4th ACM SIGIR Informational Conference on the Theory of Information Retrieval (ICTIR \\\'18), Tianjin, China, pp. 83-90, 2018.

9.Wei Zeng, Jun Xu, Yanyan Lan, Jiafeng Guo and Xueqi Cheng. Multi Page Search with Reinforcement Learning to Rank. Proceedings of the 4th ACM SIGIR Informational Conference on the Theory of Information Retrieval (ICTIR \\\'18), Tianjin, Chian, pp. 175-178, 2018.

10.Hainan Zhang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. Reinforcing Coherence for Sequence to Sequence Model in Dialogue Generation. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI \\\'18), Stockholm, Sweden, pp. 4567-4573, 2018.

11.Jizhao Zhu, Yantao Jia, Jun Xu, Qiao Jianzhong, and Xueqi Cheng. Modeling the correlations of relations for knowledge graph embedding. Journal of Computer Science and Technology, Volume 33, Issue 2, pp. 323–334, Mar. 2018.

12.Ruqing Zhang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng. Aggregating Neural Word Embeddings for Document Representation. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds) Advances in Information Retrieval. ECIR 2018, LNCS 10772, pp. 303–315, 2018.

13.Ruqing Zhang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng. Spherical Paragraph Model. In: Pasi G., Piwowarski B., Azzopardi L., Hanbury A. (eds) Advances in Information Retrieval. ECIR 2018, LNCS 10772, pp. 289–302, 2018.

14.Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, Wei Zeng, and Xueqi Cheng. Adapting Markov Decision Process for Search Result Diversification. Proceedings of the 40th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR \\\'17), Shinjuku, Tokyo, Japan, pp. 535-544, 2017.

15.Wei Zeng, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Reinforcement Learning to Rank with Markov Decision Process. Proceedings of the 40th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR \\\'17), Shinjuku, Tokyo, Japan, pp. 945-948, 2017. Short paper.

16.Yixing Fan, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. Learning Visual Features from Snapshots for Web Search. Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM \\\'17), Singapore, pp. 247-256, 2017. (CIKM 2017 Best Full Paper Runner-up)

17.Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. DeepRank: a New Deep Architecture for Relevance Ranking in Information Retrieval. Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM \\\'17), Singapore, pp. 257-266, 2017.

18.Jun Xu, Long Xia, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Directly Optimize Diversity Evaluation Measures: a New Approach to Search Result Diversification. ACM Transactions on Intelligent Systems and Technology (TIST), Volume 8, Issue 3, Article 41, pp. 41:1-41:26, Jan. 2017.

19.Liang Pang, Yanyan Lan, Jun Xu, Jiafeng Guo, and Xueqi Cheng. Locally Smoothed Neural Networks. Proceedings of the 9th Asian Conference on Machine Learning (ACML ’17), Seoul, Korea, pp. 177-191, 2017.

20.Tianyou Guo, Jun Xu, Xiaohui Yan, Jianpeng Hou, Ping Li, Zhaohui Li, Jiafeng Guo, and Xueqi Cheng. Ease the Process of Machine Learning with Dataflow. Proceedings of the 25th ACM International Conference on Information and Knowledge Management (CIKM \\\'16), Indianapolis, USA, pp. 2437-2440, 2016. Demo paper.

21.Long Xia, Jun Xu, Yanyan Lan, Jiafeng Guo, and Xueqi Cheng. Modeling Document Novelty with Neural Tensor Network for Search Result Diversification. Proceedings of the 39th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR \\\'16), Pisa, Italy, pp. 395-404, 2016.

22.Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Xueqi Cheng. A Study of MatchPyramid Models on Ad-hoc Retrieval. Proceedings of the SIGIR 2016 Workshop on Neural Information Retrieval (Neu-IR), Pisa, Italy, 2016.

23.Shengxian Wan, Yanyan Lan, Jun Xu, Jiafeng Guo, Liang Pang, Xueqi Cheng. Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN. Proceedings of the 25th International Joint Conference on Artificial Intelligence. (IJCAI \\\'16), New York, USA, pp. 2922-2928, 2016.

24.Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. Sparse Word Embeddings Using l1 Regularized Online Learning. Proceedings of the 25th International Joint Conference on Artificial Intelligence. (IJCAI \\\'16), New York, USA, pp. 2915-2921, 2016.

25.Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. Text Matching as Image Recognition. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI \\\'16), Phoenix, Arizona USA, pp. 2793-2799, 2016.

26.Shengxian Wan, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI \\\'16), Phoenix, Arizona USA, pp. 2835-2841, 2016.

27.Fei Sun, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. Inside Out: Two Jointly Predictive Models for Word Representations and Phrase Representations. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI \\\'16), Phoenix, Arizona USA, pp. 2821-2827, 2016.

28.Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, and Xueqi Cheng. SPAN: Understanding a Question with its Support Answers. Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI \\\'16), Phoenix, Arizona USA, pp. 4250-4251, 2016. poster.

29.Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. 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), San Francisco, USA, pp. 173-182, 2016.

30.Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, and Xueqi Cheng. Multi-task Representation Learning for Demographic Prediction. Proceedings of the 37th European Conference on Information Retrieval (ECIR \\\'16), Padua, Italy, pp. 88-99, 2016.

31.Shuxin Wang, Xin Jiang, Hang Li, Jun Xu, Bin Wang. Incorporating Semantic Knowledge into Latent Matching Model in Search. Proceedings of the 12th Asia Information Retrieval Societies Conference (AIRS 2016), Beijing, China, pp. 29-41, 2016.

32.Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng. Factorizing Sequential and Historical Purchase Data for Basket Recommendation. Proceedings of the 12th Asia Information Retrieval Societies Conference (AIRS 2016), Beijing, China, pp. 237-248, 2016.

33.Shengxian Wan, Yanyan Lan, Pengfei Wang, Jiafeng Guo, Jun Xu, and Xueqi Cheng. Next Basket Recommendation with Neural Networks. Proceedings of the 9th ACM Conference on Recommender Systems (RecSys \\\'15), Vienna, Austria, 2015. poster.

34.Yaogong Zhang, Jun Xu, Yanyan Lan, Jiafeng Guo, Maoqiang Xie, Yalou Huang, and Xueqi Cheng. Modeling Parameter Interactions in Ranking SVM. Proceedings of the 24th ACM International Conference on Information and Knowledge Management (CIKM \\\'15), Melbourne, Australia pp. 1799-1802, 2015. short paper.

35.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), Santiago, Chile, pp. 113-122, 2015.

36.Pengfei Wang, Jiafeng Guo, Yanyan Lan, Jun Xu, Shengxian Wan, and Xueqi Cheng. 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 \\\'15), Santiago, Chile, pp. 403-412, 2015.

37.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), Beijing, China, pp. 136-145, 2015.

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

39.Fangzhao Wu, Jun Xu, Hang Li, and Xin Jiang. Ranking Optimization with Constraints. Proceedings of the 23rd ACM Conference on Information and Knowledge Management (CIKM \\\'14), Shanghai, China, pp. 1049-1058, 2014.

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

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

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

43.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.

44.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.

45.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.

46.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.

47.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.

48.Jun Xu, Hang Li, and Chaoliang Zhong. Relevance Ranking using Kernels. Proceedings of the 6th Asia Information Retrieval Societies Conference (AIRS \\\'10), Taipei, Taiwan, pp. 1-12, 2010. (Best Paper Award)

49.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.

50.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.

51.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.

52.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.

53.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. (Nominated for SIGIR 2017 and 2018 Test of Time Award)

54.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.

55.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.

56.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.

57.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.

58.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.

59.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.

60.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.

61.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.

62.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)

授权专利

1.Jun Xu and Hang Li. Message Recommendation Method and Apparatus. WIPO Patent, Patent No. PCT/CN2015/076365. Apr. 17, 2014.

2.徐君, 李航. 分布式开发平台及其计算方法, 中国专利, CN201410273009.X, 2014.6.18.

3.Jun Xu and Hang Li. Query Expansion for Web Search. United States Patent. Patent No. US 8,898,156 B2. Date of Patent: Nov. 25, 2014.

4.Jun Xu, Hang Li, Nicholas Craswell. Regularized Latent Semantic Indexing for Topic Modeling. United States Patent. Patent No. US 8,533,195 B2. Date of Patent: Sep. 10, 2013.

5.Jun Xu, Tie-Yan Liu, Hang Li. Directly Optimizing Evaluation Measures in Learning to Rank. United States Patent. Patent No. US 8,478,748 B2. Date of Patent: Jul. 2, 2013.

6.Qing Yu, Jun Xu, Hang Li. Topics in Relevance Ranking Model for Web Search. United States Patent. Patent No. US 8,065,310 B2. Date of Patent: Nov. 22, 2011.

7.Yunbo Cao, Hang Li, Jun Xu. Ranking and Accessing Definitions of Terms. United States Patent. Patent No. US 7,877,383 B2. Date of Patent: Jan. 25, 2011.

8.Yunbo Cao, Hang Li, Jun Xu. Search by Document Type and Relevance. United States Patent. Patent No. US 7,644,074 B2. Date of Patent: Jan. 5. 2010.

9.Vladimir Tankovich, Hang Li, Dmitriy Meyerzon, and Jun Xu. Search Results Ranking using Editing Distrance and Document Information. International Patent Application. International Publication Number WO 2009/126394 A1. Oct. 15, 2009.

10.Hang Li, Jun Xu, Yunbo Cao, Tie-Yan Liu. Learning a Document Ranking Using a Loss Function with a Rank Pair or a Query Parameter. United States Patent. Patent No. US 7,593,934 B2. Date of Patent: Sep. 22, 2009.

社会兼职

1. 期刊编委:JASIST

2.Senior PC:SIGIR 2018, TheWebConf 2019 (WWW 2019), AAAI 2019, ACML 2018

3.领域主席:CCL & NLP-NABD 2018

4. CCF高级会员、CCIR常务委员、CIPS青工委委员

荣誉获奖

1.SIGIR 2017/2018 Test of Time Award Nomination

2.CCF杰出演讲者(2017年)

3.CIKM 2017 Best Full Paper Runner-up

4.AIRS 2010 best paper award, 2010

5.ICMLC 2005 best paper award, 2005

6.微软学者(2005年)