Journal of Zhejiang University SCIENCE C

, Volume 11, Issue 11, pp 903–910 | Cite as

A ranking SVM based fusion model for cross-media meta-search engine

  • Ya-li CaoEmail author
  • Tie-jun Huang
  • Yong-hong Tian


Recently, we designed a new experimental system MSearch, which is a cross-media meta-search system built on the database of the WikipediaMM task of ImageCLEF 2008. For a meta-search engine, the kernel problem is how to merge the results from multiple member search engines and provide a more effective rank list. This paper deals with a novel fusion model employing supervised learning. Our fusion model employs ranking SVM in training the fusion weight for each member search engine. We assume the fusion weight of each member search engine as a feature of a result document returned by the meta-search engine. For a returned result document, we first build a feature vector to represent the document, and set the value of each feature as the document’s score returned by the corresponding member search engine. Then we construct a training set from the documents returned from the meta-search engine to learn the fusion parameter. Finally, we use the linear fusion model based on the overlap set to merge the results set. Experimental results show that our approach significantly improves the performance of the cross-media meta-search (MSearch) and outperforms many of the existing fusion methods.

Key words

Information fusion Meta-search Cross-media Ranking 

CLC number



Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahmad, N., Sufyan Beg, M.M., 2002. Fuzzy Logic Based Rank Aggregation Methods for the World Wide Web. Int. Conf. on Arifical Intelligence in Engineering and Technology, p.363–368.Google Scholar
  2. Aslam, J.A., Montague, M., 2001. Models for Metasearch. Proc. 24th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, p.276–284. [doi:10.1145/383952.384007]Google Scholar
  3. Cao, L., Han, L.X., Wu, S.L., 2009. Ranking algorithm for meta-search engine. Appl. Res. Comput., 26(2):411–414 (in Chinese).Google Scholar
  4. Dwork, C., Kumar, R., Naor, M., Sivakumar, D., 2001. Rank Aggregation Methods for the Web. 10th Int. World Wide Web Conf., p.613–622. [doi:10.1145/371920.372165]Google Scholar
  5. Fagin, R., Kumar, R., Sivakumar, D., 2003. Efficient Similiarity Search and Classification via Rank Aggregation. Proc. ACM SIGMOD Int. Conf. on Management of Data, p.301–312. [doi:10.1145/872757. 872795]Google Scholar
  6. Fox, E.A., Shaw, J.A., 1993. Combination of Multiple Searches. The Text Retrieval Conf., p.243–252.Google Scholar
  7. Herbrich, R., Graepel, T., Obermaye, K., 2000. Large Margin Rank Boundaries for Ordinal Regression. Advances in Large Margin Classifiers, p.115–132.Google Scholar
  8. Joachims, T., 2002. Optimizing Search Engines Using Clickthrough Data. Proc. ACM Conf. on Knowledge Discovery and Data Mining (KDD), p.133–142. [doi:10.1145/775047.775067]Google Scholar
  9. Liu, T.Y., 2009. Learning to ranking for information retrieval. Found. Trends Inf. Retr., 3(3):225–331. [doi:10.1561/1500000016]CrossRefGoogle Scholar
  10. Selberg, E., Etzioni, O, 1995. Multi-Service Search and Comparison Using the Metacrawler. The 4th World Wide Web Conf., p.195–208.Google Scholar
  11. Sufyan Beg, M.M., 2004. Parrallel Rank Aggregation for the World Wide. Intelligent Sensing and Information Processing, p.385–390. [doi:10.1109/ICISIP.2004.1287 688]Google Scholar
  12. van Erp, M., Schomaker, L., 2000. Variants of the Borda Count Method for Combining Ranked Classifier Hypotheses. 7th Int. Workshop on Frontiers in Handwriting Recognition, p.443–452.Google Scholar
  13. Yu, H., Kim, S., 2010. SVM Turorial: Classification, Regression, and Ranking. In: Handbook of Natural Computing. Springer.Google Scholar
  14. Yuan, F.Y., Wang, J.D., 2009. An Implemented Rank Merging Algorithm for Meta Search Engine. Research Challenges in Computer Science, p.191–193. [doi:10.1109/ICRCCS. 2009.56]Google Scholar
  15. Zhou, Z., Tian, Y.H., Li, Y.N., Liu, T., Huang, T.J., Gao, W., 2008. PKU at ImageCLEF 2008: Experiments with Query Extension Techniques for Text-Based and Content-Based Image Retrieval. Online Working Notes for the CLEF Workshop.Google Scholar
  16. Zhou, Z., Tian, Y.H., Li, Y.N., Huang, T.J., Gao, W., 2009. Large-Scale Cross-Media Retrieval of WikipediaMM Images with Textual and Visual Query Expansion. Cross-Language Evaluation Forum, p.763–770. [doi:10.1007/978-3-642-04447-2_99]Google Scholar

Copyright information

© ?Journal of Zhejiang University Science? Editorial Office and Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Shenzhen Graduate SchoolPeking UniversityShenzhenChina
  2. 2.Institute of Digital Media, School of Electronics Engineering and Computer SciencePeking UniversityBeijingChina

Personalised recommendations