The Conclusion for sigir 2011

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The Conclusion for sigir 2011

  1. 1. The Conclusion for SIGIR 2011 Zhejiang Univ CCNT Yueshen XU
  2. 2. 目录 IR 领域的思考 1 IR 领域中知名学者与研究机构 2 会议本身的体验 3 由 SIGIR 想到的其他会议 4
  3. 3. 从 SIGIR 看当今 IR 领域的组成 Learning to Rank, Query Analysis Personalization, Retrieval Model Web IR, Image Search, Index Recommender System, Multimedia IR Vertical & Entity Research Communities, Social Media Offer Methods: CF, Classification, Clustering SIGIR/IR Traditional IR DM NLP&TM Common Latent Semantic Analysis Content Analysis, Sentiment Analysis Linguistic Analysis Multilingual IR Text Summarization Effectiveness, Efficiency
  4. 4. <ul><li>Learning to Rank </li></ul><ul><li>Query Analysis </li></ul><ul><li>Personalization </li></ul><ul><li>Retrieval Model </li></ul><ul><li>Web IR </li></ul><ul><li>Image Search </li></ul><ul><li>Index </li></ul><ul><li>Recommender System </li></ul><ul><li>Multimedia IR </li></ul><ul><li>Vertical & Entity Research </li></ul><ul><li>Communities </li></ul><ul><li>Social Media </li></ul><ul><li>Classification </li></ul><ul><li>Clustering </li></ul><ul><li>Collaborative Filtering </li></ul>当今 IR 的领域组成 Traditional IR DM New Topic <ul><li>Latent Semantic Analysis </li></ul><ul><li>Content Analysis, Sentiment Analysis </li></ul><ul><li>Linguistic Analysis </li></ul><ul><li>Multilingual IR </li></ul><ul><li>Text Summarization </li></ul>TM&NLP <ul><li>Effectiveness </li></ul><ul><li>Efficiency </li></ul>Common Field Topic Point
  5. 5. 以后怎么找点,解决问题呢 IR Learning to Rank Ranking Adaption Gradient Boosted Tree IR Retrieval Model Pseudo -Relevance Feedback Boosting Approach Field Topic Point Method Field From Papers Field From Papers
  6. 6. 想出的一点研究层次 Research Levels Point Topic Field Discipline <ul><li>From Papers </li></ul><ul><li>Be cherished </li></ul><ul><li>From tutor or experience </li></ul><ul><li>IR,DM,AI,TM,DB,DC </li></ul>Discipline Field Topic Point <ul><li>From Conference </li></ul><ul><li>Vital </li></ul><ul><li>From your mind </li></ul><ul><li>App Math or ..., Not concern </li></ul>
  7. 7. 由 SIGIR 形成对 IR 的基本认识 Application System Demo Deployment etc. Methodology Problem Relevance Feedback Ranking Adaption Active Query etc. Object of Research in IR Algorithm Mathematic Strategy what we should concern about what those companies are interested in obtain from those papers
  8. 8. 对 IR 中方法论的认识 Method-logy Algorithm Mathe -matic Strategy Mathe -matic Data Structure ! Index etc. Text Semantic Analysis etc. Probability Model, CF, Clustering, Classification etc.------prevail Architecture, Procedure,-------informal method, associating with corporations and application
  9. 9. 从 SIGIR 中的 session 看 problem Data Close to DM Medium Text, Image, Multimedia Inherence Data Structure is vital. Other deployment, linguistic etc. What should we model and research? Probability Model CF Clustering Classification Text Mining, Content Analysis Social Media Text Summarization Sentiment Analysis Ranking Query Index Retrieval Model Image Search Vertical & Entity Search Interested in by companies
  10. 10. <ul><li>NDCG @N </li></ul><ul><li>MAP </li></ul><ul><li>[email_address] </li></ul><ul><li>BM 25/BM 25F </li></ul><ul><li>TF-IDF </li></ul><ul><li>Etc. </li></ul><ul><li>LETOR 3.0/4.0:Learning to Rank </li></ul><ul><li>TREC web collection </li></ul><ul><li>.Gov2 </li></ul><ul><li>Blog06/08 </li></ul><ul><li>WT2g/10g </li></ul><ul><li>Clue Web 9 </li></ul><ul><li>AP88~89 </li></ul><ul><li>COREL: a real image data set </li></ul>从评估与实验中看标准化 Ranking Relevance Web/Log Collections Assess with Classical Indicator Test with Standard Data Set <ul><li>But some companies take advantage of their own data sets, </li></ul><ul><li>just like Yahoo! and Google. </li></ul>Fee!
  11. 11. <ul><li>York University IR Lab(Jimmy Huang) </li></ul><ul><li>Two papers in one session </li></ul><ul><li>Dalian Tech Univ, IR Lab </li></ul><ul><li>One full paper, one poster </li></ul><ul><li>Peking Univ, IR Lab </li></ul><ul><li>Two full papers in one session </li></ul><ul><li>etc. </li></ul>从普通大学的表现看团队的重要性 <ul><li>A research team which pursues one aim has much potential. </li></ul>
  12. 12. 目录 IR 领域的思考 1 IR 领域中知名学者与研究机构 2 会议本身的体验 3 由 SIGIR 想到的其他会议 4
  13. 13. 本次会议中的知名华人学者 ( 部分 ) Rong Jin MSU Tutorials invited speaker Statistical learning etc. Luo Si Purdue Univ Tutorials invited speaker Intelligent tutoring, text mining for life science etc. Chengxiang Zhai UIUC Keynote invited speaker Text Mining, Machine Learning etc. Tie-Yan Liu MSRA Session Chair & Workshop chair Learning to rank, Large-scale graph learning etc.
  14. 14. 本次会议中的知名国外学者 ( 部分 ) W.Bruce Croft UMA Program Co-chair Session chair Workshop chair Salton Award Stephen Robertson MS and London City Univ Salton Award Susan Dumais MS Outstanding paper award chair Salton Award Paul B. Kantor Rutgers University Tutorial invited speaker Distinguished professor of Information Science  (Wikipedia)
  15. 15. IR 领域中知名的研究机构 <ul><li>Involved in10 papers at SIGIR 34th </li></ul><ul><li>Gold sponsor of SIGIR 34th </li></ul><ul><li>Involved in 19 papers at SIGIR 34th </li></ul><ul><li>Diamond sponsor of SIGIR 34th </li></ul>Universities and Research Labs <ul><li>Involved in 4 papers at SIGIR 34th </li></ul><ul><li>Bronze sponsor of SIGIR 34th </li></ul>
  16. 16. 目录 IR 领域的思考 1 IR 领域中知名学者与研究机构 2 会议本身的体验 3 由 SIGIR 想到的其他会议 4
  17. 17. <ul><li>Tutorial </li></ul><ul><li>Introduce those fundamental knowledge, new progresses or some history. </li></ul><ul><li>Main Conference </li></ul><ul><li>Expound your own paper and evaluate others’ work. </li></ul><ul><li>Workshop </li></ul><ul><li>Discuss about some interesting topics </li></ul><ul><li>Coffee break </li></ul><ul><li>Eat, drink, relax and discuss. </li></ul><ul><li>Keynote speech, Industry track, registration etc. </li></ul>知晓了会议的各个组成部分
  18. 18. <ul><li>Speaker </li></ul><ul><li>Make people understand your definite meaning. </li></ul><ul><li>Understand the queries from questioners. </li></ul><ul><li>Audience </li></ul><ul><li>Prepare well. </li></ul><ul><li>Understand what the speaker has done exactly. </li></ul><ul><li>Give him/her a question </li></ul>英语的重要性 <ul><li>I am very sorry that my listening English is not so good.  </li></ul>
  19. 19. <ul><li>Tight Schedule </li></ul><ul><li>From day to night </li></ul><ul><li>One paper after another </li></ul><ul><li>Parallel </li></ul><ul><li>Mass Information and Mathematic </li></ul><ul><li>Just like etc. </li></ul><ul><li>Uninterruptable procedure </li></ul>身体的重要性 <ul><li>You may be worn down if you are involved in it totally. </li></ul>
  20. 20. 目录 IR 领域的思考 1 IR 领域中知名学者与研究机构 2 会议本身的体验 3 由 SIGIR 想到的其他会议 4
  21. 21. <ul><li>ACM Special Interest Group on Knowledge Discovery and Data Mining </li></ul><ul><li>8 .21~8.24, 2011 , San Diego , CA </li></ul><ul><li>Rank 1 </li></ul>由 SIGIR 想到的其他会议 SIGKDD DM & IR ICDM <ul><li>IEEE International Conference onData Mining </li></ul><ul><li>12 .11~12.14, 2011, Vancouver, Canada </li></ul><ul><li>Rank 1~ </li></ul>
  22. 22. <ul><li>ACM Conference on Information and knowledge management </li></ul><ul><li>10.24~10.28, 2011,Glasgow, UK </li></ul><ul><li>Rank 1 </li></ul>由 SIGIR 想到的其他会议 CIKM DM & IR WSDM <ul><li>ACM International Conference on Web Search and Data Mining </li></ul><ul><li>2.8~2.19, 2012,Washington, USA </li></ul><ul><li>? ! Young but high quality </li></ul>
  23. 23. <ul><li>International World Wide Web Conference </li></ul><ul><li>4.16~4.20, 2012Lyon, France </li></ul><ul><li>Rank 1 </li></ul>由 SIGIR 想到的其他会议 WWW DM & IR PAKDD <ul><li>Pacific-Asia Conference on Knowledge Discovery and Data Mining </li></ul><ul><li>5.29~56.1 , 2012 , Kuala Lumpur, Malaysia </li></ul><ul><li>Rank 2 </li></ul>TREC? ISWC MLDM ICDE PKDD etc.
  24. 24. 总结与展望 <ul><li>Appreciate the guidance and help from Wei Luo </li></ul><ul><li>Study mathematic and fundamental concepts </li></ul><ul><li>Discovery, Read, Write and Publish Papers </li></ul>

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