Welcome to Journal of Beijing Institute of Technology
Advance Search
Volume 23Issue 2
.
Turn off MathJax
Article Contents
Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2014> 23(2): 240-247
CHEN Fang-qiong, YU Zheng-tao, WU Ze-jian, MAO Cun-li, ZHANG You-ming. Expert ranking method based on ListNet with multiple features[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2014, 23(2): 240-247.
Citation: CHEN Fang-qiong, YU Zheng-tao, WU Ze-jian, MAO Cun-li, ZHANG You-ming. Expert ranking method based on ListNet with multiple features[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2014, 23(2): 240-247.
shu

Expert ranking method based on ListNet with multiple features

  • School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China;Intelligent Information Processing Key Laboratory, Kunming University of Science and Technology, Kunming 650500, China

  • Received Date:2013-02-16
    Abstract
  • The quality of expert ranking directly affects the expert retrieval precision. According to the characteristics of the expert entity, an expert ranking model based on the list with multiple features was proposed. Firstly, multiple features was selected through the analysis of expert pages; secondly, in order to learn parameters through gradient descent and construct expert ranking model, all features were integrated into ListNet ranking model; finally, expert ranking contrast experiment will be performed using the trained model.The experimental results show that the proposed method has a good effect, and the value of NDCG@1 increased 14.2% comparing with the pairwise method with expert ranking.
    • FullText(HTML)
  • loading
    • References (10)
  • [1]
    Fang Yi, Si Luo, Mathur A. Learning to rank expertise information in heterogeneous information sources[C]//Proceedings of SIGIR 2009 Workshop on Learning to Rank for Information Retrieval, Boston, USA, 2009.
    [2]
    Liu Tieyan, Xu Jun, Qin Tao, et al. Letor: Benchmark dataset for research on learning to rank for information retrieval[C]//Proceedings of SIGIR 2007 Workshop on Learning to Rank for Information Retrieval, Amsterdam, The Netherlands, 2007.
    [3]
    Nallapati R. Discriminative models for information retrieval[C]//Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Sheffield, United Kingdom, 2004: 64-71.
    [4]
    Burges C, Shaked T, Renshaw E, et al. Learning to rank using gradient descent[C]//Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany, 2005.
    [5]
    Cao Zhe, Qin Tao, Liu Tieyan, et al. Learning to rank:from pairwise approach to listwise approach[C]//Proceedings of the 24th International Conference on Machine Learning, Corvallis, Oregon, 2007: 129-136.
    [6]
    Cao Y, Xu Jun, Liu Tieyan, et al. Adapting ranking SVM to document retrieval[C]//Proceedings of SIGIR2006, Seattle, USA,2006.
    [7]
    Fang Yi, Si Luo, Mathur A. Discriminative models of integrating document evidence and document-candidate associations for expert search[C]//Proceeding of the 33rd international ACM SIGIR, Geneva, Switzerland, 2010: 683-690.
    [8]
    Baeza-Yates R, Ribeiro-Neto B. Modern information retrieval[M]. New York: Addison Wesley, 1999.
    [9]
    Robertson S E, Walker S, Hancock-Beaulieu M, et al. Okapi in TREC3[C]//Proceedings of Text Retrieval Conference, Gaithersburg: USA U.S. National Institute of Standards and Technology, 1994: 109-126.
    [10]
    Zhou Dengyong, Weston J, Gretton A, et al. Ranking on data manifolds[C]//Proceedings of 16th Advances in Neural Information Processing Systems, British Columbia, Canada, 2003: 16-169.
    • Relative Articles
  • Supplements (0)
  • Cited By
  • 加载中

Catalog

    通讯作者:陈斌, bchen63@163.com
    • 1.

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML

    Article Metrics

    Article views (428) PDF downloads(98) Cited by()
    Proportional views
    Related

    Address:5 Zhongguancun South Street, Haidian District, Beijing

    Tel:86-10-68914374

    Email:blgywb@bit.edu.cn

    Copy Right © Journal of Beijing Institute of Technology

    Supported by:Beijing Renhe Information Technology Co. Ltd

    /

      Return
      Return
        Baidu
        map