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SCIENCE CHINA Information Sciences, Volume 61, Issue 12: 122102(2018) https://doi.org/10.1007/s11432-017-9343-5

LionRank: lion algorithm-based metasearch engines for re-ranking of webpages

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  • ReceivedOct 18, 2017
  • AcceptedMay 22, 2018
  • PublishedNov 20, 2018

Abstract

Due to the rapid growth of the web, the process ofcollecting the relevant web pages based on the user query is one of themajor challenging tasks in recent days. Hence, it is very complicated forthe users to know the most relevant information even though various searchengines are widely employed. To deal with users' trouble in identifying therelevant information from the web, we have proposed a meta-lion searchengine to capture and analyze the ranking scores of various search enginesand thereby, generate the re-ranked score results. Accordingly, LionRank, alion algorithm-based meta-search engine is proposed for the re-ranking ofthe web pages. Here, different features like text based, factor based, rankbased and classifier based features are used by the underlying searchengines. In classifier based feature extraction, we have used the fuzzy integrated extended nearest neighbor (FENN) classifier to include thesemantics in feature extraction. Moreover, an intelligent re-ranking processis proposed based on the lion algorithm to fuse the features scoresoptimally. Finally, the results of the proposed LionRank is analyzed withthe web page database collected through four benchmark queries, and thequantitative performance are analyzed using precision, recall, and F-score.From the results, we proved that the proposed LionRank obtained the maximumF-score of 81%as compared with that of existing search engines likeQuadRank, Outrank, Google, Yahoo, and Bing.


References

[1] Naim I, Ali R. Metasearching using modified rough set based rank aggregation. In: Proceedings of International Conference on Multimedia, Signal Processing and Communication Technologies, Aligarh, 2011. 208--211. Google Scholar

[2] Keyhanipour A H, Moshiri B, Piroozmand M, et al. WebFusion: fundamentals and principals of a novel meta search engine. In: Proceedings of International Joint Conference on Neural Networks, Vancouver, 2006. 4126--4131. Google Scholar

[3] Sumiya K, Kitayama D, Chandrasiri N P. Inferred information retrieval with user operations on digital maps. IEEE Int Comput, 2014, 18: 70-73 CrossRef Google Scholar

[4] Davison B D. The potential of the meta-search engine. In: Proceedings of the Annual Meeting of the American Society for Information Science and Technology, Providence, 2004. 393--402. Google Scholar

[5] Sun Y Z, Han J W. Meta-path-based search and mining in heterogeneous information networks. Tinshhua Sci Technol, 2013, 18: 329-338 CrossRef Google Scholar

[6] Cao Y L, Huang T J, Tian Y H. A ranking SVM based fusion model for cross-media meta-search engine. J Zhejiang Univ Sci C, 2010, 11: 903-910 CrossRef Google Scholar

[7] Keyhanipour A H, Moshiri B, Kazemian M. Aggregation of web search engines based on users' preferences in WebFusion. Knowl-Based Syst, 2007, 20: 321-328 CrossRef Google Scholar

[8] Desarkar M S, Sarkar S, Mitra P. Preference relations based unsupervised rank aggregation for metasearch. Expert Syst Appl, 2016, 49: 86-98 CrossRef Google Scholar

[9] Ma S X, Li S Y, Yang H J. Creative computing for personalisedmeta-search engine based on semantic web. In: Proceedings of the 21st International Conference on Automation and Computing, Glasgow, 2015. Google Scholar

[10] Keyhanipour A H, Moshiri B, Lucas C. User modeling for the result re-ranking in the meta-search engines via the reinforcement learning. In: Proceedings of the 7th International Conference on Intelligent Systems Design and Applications, Rio de Janeiro, 2007. Google Scholar

[11] Lange S, Gebert S, Zinner T. Heuristic approaches to the controller placement problem in large scale SDN networks. IEEE Trans Netw Serv Manage, 2015, 12: 4-17 CrossRef Google Scholar

[12] Keyhanipour A H, Moshiri B, Lucas C. User modelling for the result re-ranking in the meta-search engines via the reinforcement learning. In: Proceedings of the 7th International Conference on Intelligent Systems Design and Applications, Rio de Janeiro, 2007. Google Scholar

[13] Mavridis T, Symeonidis A L. Identifying valid search engine ranking factors in a Web 2.0 and Web 3.0 context for building efficient SEO mechanisms. Eng Appl Artif Intel, 2015, 41: 75-91 CrossRef Google Scholar

[14] Huang J, Yang X K, Fang X Z. Integrating visual saliency and consistency for re-ranking image search results. IEEE Trans Multimedia, 2011, 13: 653-661 CrossRef Google Scholar

[15] Hassanpour H, Zahmatkesh F. An adaptive meta-search engine considering the user's field of interest. J King Saud Univ Comput Inf Sci, 2012, 24: 71-81 CrossRef Google Scholar

[16] Vargas-Vera M, Castellanos Y, Lytras M D. CONQUIRO: a cluster-based meta-search engine. Comput Human Behav, 2011, 27: 1303-1309 CrossRef Google Scholar

[17] Rajakumar B R. Lion algorithm for standard and large scale bilinear system identification: a global optimization based on lion's social behaviour. In: Proceedings of Congress on Evolutionary Computation, Beijing, 2014. Google Scholar

[18] Rohini U, Varma V. A novel approach for re-ranking of search results using collaborative filtering. In: Proceedings of the International Conference on Computing: Theory and Applications, Kolkata, 2007. 491--496. Google Scholar

[19] Karisani P, Rahgozar M, Oroumchian F. A query term re-weighting approach using document similarity. Inf Process Manage, 2016, 52: 478-489 CrossRef Google Scholar

[20] Akritidis L, Katsaros D, Bozanis P. Effective rank aggregation for metasearching. J Syst Softw, 2011, 84: 130-143 CrossRef Google Scholar

[21] Muller E, Assent I, Steinhausen U, et al. OutRank: ranking outliers in high dimensional data. In: Proceedings of IEEE International Conference on Data Engineering Workshops, Cancun, 2008. 600--603. Google Scholar

[22] Xu J, Li H. Adarank: a boosting algorithm for information retrieval. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and development in Information Retrieval, Amsterdam, 2007. 391--398. Google Scholar

[23] Iraji M S, Maghamnia H, Iraji M. Web pages retrieval with adaptive neuro fuzzy system based on content and structure. Modern Educ Comput Sci, 2015, 7: 69-84 CrossRef Google Scholar

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