Jun Xu

(徐君)

Professor, Ph.D.

CAS Key Lab of Web Data Science and Technology

Institute of Computing Technology

Chinese Academy of Sciences

  • Email: junxu AT ict.ac.cn
  • Address: No. 6 Kexueyuan South Road, Zhongguancun, Haidian District, Beijing, China   100190
About Me

I received my B.E. and Ph.D. in Computer Science from Nankai University, in 2001 and 2006, respectively. I worked as an associate researcher, researcher, and senior researcher at Microsoft Research Asia and Huawei Noah's Ark Lab. In 2014, I joined Institute of Computing Technology, Chinese Academy of Sciences. My research interests are in information retrieval, machine learning, and big data analysis. I have worked on (i) learning to rank for information retrieval; (ii) large scale topic modeling; and (iii) semantic matching in search.

Google Scholar Citations   DBLP   ACM   Chinese Version

Teaching

Book

Semantic Matching in Search by Hang Li and Jun Xu, Now Publishers, 2014. (pdf, link)

Open Source

Publications
  1. 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) (pdf)
  2. 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) (short paper, pdf)
  3. 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. (pdf, appendix)
  4. 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. (pdf, demo)
  5. 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. (pdf, source code)
  6. 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. (pdf, slides)
  7. 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. (pdf)
  8. 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. (pdf)
  9. 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. (pdf)
  10. 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. (pdf)
  11. 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. (pdf)
  12. 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. (pdf)
  13. 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. (pdf)
  14. 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. (pdf)
  15. 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. (pdf)
  16. 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. (pdf)
  17. 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.
  18. 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. (pdf, poster)
  19. 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. (pdf, source code)
  20. 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. (pdf, source code and data)
  21. 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. (pdf, source code and data)
  22. 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. (pdf)
  23. 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. (pdf)
  24. 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. (pdf)
  25. Hang Li and Jun Xu. Semantic Matching in Search. Foundations and Trends in Information Retrieval 7(5): 343-469, Now Publishers, 2014. (pdf)
  26. 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. (pdf)
  27. 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. (pdf)
  28. 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. (pdf)
  29. 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. (pdf, source code)
  30. 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. (pdf)
  31. 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. (pdf)
  32. 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, pdf)
  33. 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. (pdf, LETOR datasets)
  34. 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. (pdf)
  35. 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. (pdf)
  36. 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. (pdf)
  37. 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. (pdf, binary code) (Automatically Nominated for SIGIR 2017 Test of Time Award, link)
  38. 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. (pdf)
  39. 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. (pdf)
  40. 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)
  41. 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. (pdf)
  42. 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. (pdf)
  43. 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. (pdf)
  44. 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. (pdf)
  45. 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. (pdf)
  46. 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)
Patents

  1. 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. (pdf)
  2. 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. (pdf)
  3. 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. (pdf)
  4. 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. (pdf)
  5. 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. (pdf)
  6. 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. (pdf)
  7. 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. (pdfs: PCT, US (granted on Aug. 19, 2014), CN (granted on Dec. 18, 2013), JP, KR, RU, and TW)
  8. 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. (pdf)
Talks Awards Services Others

Date of update: Jun. 28, 2017