Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Research on optimizing the merging results of multiple independent retrieval systems by a discrete particle swarm optimization

  • Published:
Journal of Electronics (China)

Abstract

The result merging for multiple Independent Resource Retrieval Systems (IRRSs), which is a key component in developing a meta-search engine, is a difficult problem that still not effectively solved. Most of the existing result merging methods, usually suffered a great influence from the usefulness weight of different IRRS results and overlap rate among them. In this paper, we proposed a scheme that being capable of coalescing and optimizing a group of existing multi-sources-retrieval merging results effectively by Discrete Particle Swarm Optimization (DPSO). The experimental results show that the DPSO, not only can overall outperform all the other result merging algorithms it employed, but also has better adaptability in application for unnecessarily taking into account different IRRS’s usefulness weight and their overlap rate with respect to a concrete query. Compared to other result merging algorithms it employed, the DPSO’s recognition precision can increase nearly 24.6%, while the precision standard deviation for different queries can decrease about 68.3%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Ganzha, M. Paprzycki, and J. Stadnik. Combining information from multiple search engines -Preliminary comparison. Information Science, 180(2010)10, 1908–1923.

    Article  Google Scholar 

  2. Fuyong Yuan and Jindong Wang. An implemented rank merging algorithm for meta search engine. IEEE International Conference on Research Challenges in Computer Science, Washington, D.C., USA, December 2009, 191–193.

  3. Shengli Wu and S. McClean. Result merging methods in distributed information retrieval with overlapping databases. ACM International Workshop on Multimedia Information Retrieval, University of Augsburg, Germany, September 2007, 297–319.

    Google Scholar 

  4. Yiyao Lu, Weiyi Meng, Liangcai Shu, et al. Evaluation of result merging strategies for meta-search engines. Lecture Notes in Computer Science, 3806(2005)18, 53–66.

    Article  Google Scholar 

  5. Shengli Wu and F. Crestani. Shadow document methods of results merging. Proceedings of the ACM Symposium on Applied Computing, Nicosia, Cyprus, March 2004, 1067–1072.

  6. T. Danet and L. P. Dinu. Meta-search: aggregating search engine results using rank-distance. ACM International Conference on Similarity Search and Applications, Lipari, Italy, June 2011, 6–11.

  7. L. P. Dinu and F. Manea. An efficient approach for the rank aggregation problem. Theoretical Computer Science, 359(2006)1–3, 455–461.

    Article  MathSciNet  MATH  Google Scholar 

  8. Y. Shi and R. Eberhart. A modified particle swarm optimizer. IEEE International Conference on Computational Intelligence-Evolutionary Computation, Anchorage, AK, USA, May 1998, 69–73.

  9. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficient. IEEE Transactions on Evolutionary Computation, 8(2004)3, 240–255.

    Article  Google Scholar 

  10. Qingyun Yang, Chunjie Wang, and Changsheng Zhang. An efficient discrete particle swarm algorithm for task assignment problems. IEEE International Conference on Granular Computing, Nanchang, China, August 2009, 686–690.

  11. Yu Linli and Cai Zixing. Multiple optimization strategies for improving hybrid discrete particle swam. Journal of Central South University, 40(2009)4, 1047–1053 (in Chinese). 余伶俐, 蔡自兴. 改进混合离散粒子群的多种优化策略算法. 中南大学学报(自然科学版), 40(2009)4, 1047–1053.

    MathSciNet  Google Scholar 

  12. Fen Xia, Tieyan Liu, Jun Wang, et al. Listwise approach to learning to rank-theory and algorithm. ACM International Conference on Machine Learning, Helsinki, Finland, July 2008, 1192–1199.

  13. Zhe Cao, Qin Tao, Tieyan Liu, et al. Learning to rank: from pairwise approach to listwise approach. ACM International Conference on Machine Learning, Oregon, USA, June 2007, 258–263.

  14. Guo Qinglin, Li Yanmei, and Tang Qi. Similarity computing of documents based on VSM. Journal of Application Research of Computers, 25(2008)11, 3256–3258 (in Chinese). 郭庆琳, 李艳梅, 唐琦. 基于VSM的文本相似度计算的研究. 计算机应用研究, 25(2008)11, 3256–3258.

    Google Scholar 

  15. Dou Sheny, Toby Walkery, Zijian Zhengy, et al. Personal name classification in web queries. ACM International Conference on Web Search and Web Data Mining, Palo Alto, CA, USA, February 2008, 149–158.

  16. F. Calderero and F. Marques. Region merging techniques using information theory statistical measures. IEEE Transactions on Image Processing, 19(2010)6, 1567–1586.

    Article  MathSciNet  Google Scholar 

  17. http://scholar.google.com. March 18–20, 2011.

Download references

Author information

Authors and Affiliations

  1. Automation Department, University of Science and Technology of China, Hefei, 230027, China

    Xingsheng Xie & Guoliang Zhang

  2. Computer Department, University of Science and Technology of China, Hefei, 230027, China

    Yan Xiong

Authors
  1. Xingsheng Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoliang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xingsheng Xie.

Additional information

Supported by the National Natural Science Foundation of China (No. 90818007).

Communication author: Xie Xingsheng, born in 1965, male, Associate Professor.

About this article

Cite this article

Xie, X., Zhang, G. & Xiong, Y. Research on optimizing the merging results of multiple independent retrieval systems by a discrete particle swarm optimization. J. Electron.(China) 29, 111–119 (2012). https://doi.org/10.1007/s11767-012-0751-9

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11767-012-0751-9

Key words

CLC index

Search

Navigation