期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

信息检索排序算法研究综述 被引量：3

Survey of information retrieval ranking algorithm
下载PDF
导出
摘要 排序技术是信息检索系统进行结果处理的核心技术,排序算法的优劣直接影响系统的效率。将现有的排序算法分为基于链接分析和基于机器学习两大类,系统地分析了各自代表性算法,指出它们各自的优势和存在的不足,并指出不同算法在不同领域和场合所具有的优势,最后讨论可继续研究的课题。 The ranking technology is a core technology of tire result reduction for inforn^tion retrieval system, and the ranking algorithm directly influence its efficacy. The two kinds of ranking algorithms are link analysis-based and machine learning-based. This paper systematically analyzed representative ranking algorithms respectively and pointed out their advantages and deficiency, different ranking algorithms had different performance in different domains, discussed the future issues at last.
作者 高炜 张超 梁立
机构地区 云南师范大学计算机科学与信息技术学院
出处 《信息技术》 2009年第6期1-4,共4页 Information Technology
基金 云南省自然科学基金重点项目(04F00062)
关键词 信息检索 PAGERANK算法 HITS算法 RLR算法 information retrieval PageRank algorithm HITS algorithm RLR algorithm
分类号 TP393.092 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献20

  • 1Page L. The PageRank Citation Ranking: Bring Order to the Web[EB/ OL]. Stanford Digital Libraries Working Paper. [1999]. http://www. diglib. stanford. edu.
  • 2Kleinberg J M. Authoritative sources in a hyperlinked environment[J]. Journal of the ACM, 1999,46(5) :604 - 632.
  • 3Nallapati R. Discriminative Modds for Information Retrieval[C]. Proceedings of the 27th SIGIR conference, on information retrieval,2004: 64-71.
  • 4Caruana R, Baluja S, Mitchell T. Using the future to "sort out" the present:~ and multitask learning for medical risk evaluation[J]. Advances in Neural information Processing Systems (NIPS)8: 959 - 965.
  • 5Freund Y, lyer R D, Schapire R E, et al. An efficient boosting algorithm for combining preferences[ C ]. Proceedings of the 15th Intl. conference on machine learning , San Francisco, CA, USA, 1998:170 - 178.
  • 6Joachims T. Optimizing search engines using clickthrongh data [ C ]. proceedings of the 8th ACM SIGKDD intl. conference, on knowledge discovery and data mining. New York, NY, USA, ACM press,2002: 133 - 142.
  • 7Herbrich R, Graepel T, Obermayer K. Large margin rank boundaries for ordinal regression[ J ]. Advances in Large Marge Classifiers. MIT Press,2000:115- 132.
  • 8Burges C, et al. Leaming to rank using gradient descent[C]. proceedings of the 22nd intl. conf. on machine learning, 2005:89-96.
  • 9T-S.Chua, S-Y.Neo, H-K.C, oh,et al.Trecvid 2005 by nus pris[J]. NIST TRECVID, Nov,2005.
  • 10Rong Yan, Alexander, Hauptmann D. Efficient Margin-Based Rank Leaming Algorithms for Information Retrieval[J]. CIVR 2006, LNCS 4071 : 113 - 122.

二级参考文献9

  • 1钱榕,徐新华,郑莹,杨炳儒.智能专题化信息搜集Crawler[J].计算机工程,2006,32(3):57-59. 被引量：4
  • 2Brin S,Page L.The Anatomy of a Large-scale Hypertextual Web Search Engine.In Proc.of WWW8,Brieman(Australia),1998,4:107-117.
  • 3Page L,Brin S,Motwani R,et al.The PageRank Citation Ranking:Bringing Order to the Web.Stanford Digital Libraries Working Paper,1998.
  • 4Kosala R,Blockeel H.Web Mining Research:A Survey.ACM SIGKDD,2000,7.
  • 5Kleinberg J.Authoritative Sources in a Hyperlinked Environment.Proc.of 9th ACMSIAM Symposium on Discrete Algorithms.Also Appeared as IBM Research Report RJ 10076,1997,5.
  • 6Chakrabarti S,Dom B E,Gibson D,et al.Mining the Link Structure of the World Wide Web.IEEE Computer,1999,32(8).
  • 7Lempel R,Moran S.SALSA:stochastic app roach for link 2 structure analysis and the TKC effect[J].ACM Trans.Information Systems,2001,19:131-160.
  • 8Langville A N,Meyer C D.A Survey of Eigenvector Methods for Web Information Retrieval.SIAM Rev.2005,47:135-161.
  • 9Borodin A,Roberts G,Rosenthal J,Tsaparas P.Link analysis ranking:algorithms,theory,and experiments.ACM Trans.Inter.Tech.2005,5(1):231-297.

共引文献4

同被引文献33

引证文献3

二级引证文献5

信息技术
2009年 第6期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部