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CLC number: TP391

On-line Access: 2010-11-04

Received: 2010-09-14

Revision Accepted: 2010-10-09

Crosschecked: 2010-09-26

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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.11 P.903-910


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

Author(s):  Ya-li Cao, Tie-jun Huang, Yong-hong Tian

Affiliation(s):  Shenzhen Graduate School, Peking University, Shenzhen 518055, China, Institute of Digital Media, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

Corresponding email(s):   ylcao@jdl.ac.cn

Key Words:  Information fusion, Meta-search, Cross-media, Ranking

Ya-li Cao, Tie-jun Huang, Yong-hong Tian. A ranking SVM based fusion model for cross-media meta-search engine[J]. Journal of Zhejiang University Science C, 2010, 11(11): 903-910.

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journal="Journal of Zhejiang University Science C",
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%T A ranking SVM based fusion model for cross-media meta-search engine
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T1 - A ranking SVM based fusion model for cross-media meta-search engine
A1 - Ya-li Cao
A1 - Tie-jun Huang
A1 - Yong-hong Tian
J0 - Journal of Zhejiang University Science C
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Y1 - 2010
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1001009

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


[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.

[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.

[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).

[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.

[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.

[6]Fox, E.A., Shaw, J.A., 1993. Combination of Multiple Searches. The Text Retrieval Conf., p.243-252.

[7]Herbrich, R., Graepel, T., Obermaye, K., 2000. Large Margin Rank Boundaries for Ordinal Regression. Advances in Large Margin Classifiers, p.115-132.

[8]Joachims, T., 2002. Optimizing Search Engines Using Clickthrough Data. Proc. ACM Conf. on Knowledge Discovery and Data Mining (KDD), p.133-142.

[9]Liu, T.Y., 2009. Learning to ranking for information retrieval. Found. Trends Inf. Retr., 3(3):225-331.

[10]Selberg, E., Etzioni, O, 1995. Multi-Service Search and Comparison Using the Metacrawler. The 4th World Wide Web Conf., p.195-208.

[11]Sufyan Beg, M.M., 2004. Parrallel Rank Aggregation for the World Wide. Intelligent Sensing and Information Processing, p.385-390.

[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.

[13]Yu, H., Kim, S., 2010. SVM Turorial: Classification, Regression, and Ranking. In: Handbook of Natural Computing. Springer.

[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.

[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.

[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.

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