Abstract
Metasearch engines always play a crucial role to provide useful information for the requested query. At the hinder end, the rank aggregation (RA) module is perhaps the most important component which merges the output derived from distinct search engines. But the primary goal of this problem is to assign a relevance rank to similar documents from different search engines in order to select the best optimized document. With this application in mind, we have proposed moth search algorithm (MSA)-based approach along with two distance measure methods. Thus, Spearman’s footrule and Kendall tau distance measures are optimized which are further applied to assign ranks to the documents by different rank aggregation methods. Experimentally, it has been proved that MSA approach outperformed than conventional genetic algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
L. Akritidis, D. Katsaros, P. Bozanis, Effective rank aggregation for metsearching. J. Syst. Softw. 84, 130–143 (2011)
K. Arrow, Social Choice and Individual Values (Wiley, New York, 1951)
M.M.S. Beg, N. Ahmad, Soft computing techniques for rank aggregation on the World Wide Web. J. Int. Inf. Syst. 6(1), 5–22 (2003)
M.M.S. Beg, N. Ahmad, Study of rank aggregation for world wide web. J. Study Fuzziness Soft Comput. 137, 24–46 (2004)
M.M.S. Beg, N. Ahmad, Fuzzy logic based rank aggregation methods for the world wide web, in Proceedings of the International Conference on Artificial Intelligence in Engineering and Technology (Malaysia, 2002), pp. 363–368
J.C. Borda, Mémoire sur les élections au scrutin. Histoire de l’Académie Royale des Sciences (1781)
P. Diaconis, R. Graham, Spearman’s footrule as a measure of disarray. J. Roy. Stat. Soc. B 39(2), 262–268 (1977)
C. Dwork, R. Kumar, M. Naor, D. Sivakumar, Rank aggregation methods for the web, in Proceedings of Tenth ACM International Conference on World Wide Web (2001), pp. 613–622
G.-G. Wang, Moth search algorithm: A bio-inspired metaheuristic algorithm for global Optimization problems. Memetic Comput. 1–14 (2016). https://doi.org/10.1007/s12293-016-0212-3
D.E. Goldberg, Book on Genetic Algorithms in Search, Optimization and Machine Learning (Addison Wesley, 1989)
J. Holland, Genetic algorithm and adaptation, in Adaptive Control of Ill-Defined Systems Vol. 16 of the Series NATO Conference Series (1975) pp. 317–333
P. Kaur, M. Singh, G.S. Josan, Comparative analysis of rank aggregation techniques for metasearch using genetic algorithm. Springer J. Educ. Inf. Technol. 21, 1–19 (2016)
P. Kaur, M. Singh, G.J. Singh, S.S. Dhillon, Rank aggregation using ant colony approach for metasearch. J. Soft Comput. Springer 22(3), 4477–4492 (2018)
A. Laughlin, J. Olson, D. Simpson, A. Inoue, Page ranking refinement using fuzzy sets and logic, in Proceedings of the 22nd Midwest Artificial Intelligence and Cognitive Science Conference (Cincinnati, Ohio, USA, 2011), pp. 40–46
A.N. Langville, C.D. Meyer, Book of Who’s #1?: The Science of Rating and Ranking (Princeton University Press, Princeton, NJ, USA, 2012)
L. Xue, W. Guanghua, A. Xiao, Comparative study of rank aggregation methods for partial and top ranked lists in genomic applications. Brief. Bioinf. 20(1), 178–189 (2019)
M.H. Montague, J.A. Aslam, Models of metasearch, in Proceedings of the ACM International Conference on Research and Development in Information Retrieval (SIGIR, 2001), pp. 276–284
G. Napoles, Z. Dikopoulou, E. Papgeorgiou, R. Bello, K. Vanhoof, Aggregation of partial rankings-an approach based on the Kemeny ranking problem. Adv. Comput. Intell. Lect. Notes Comput. Sci. 9095, 343–355 (2015)
L. Page, L. Brin, The anatomy of a large-scale hyper textual web search engine, in Proceedings Of Seventh International World Wide Web Conference (1998)
V. Pihur, S. Datta, Rank aggregation, an R package for weighted rank aggregation. A Report by Department of Bioinformatics and Biostatistics, University of Louisville (2014). http://vpihur.com/biostat
M.E. Renda, U. Straccia, Web metasearch: rank vs. score based rank aggregation methods, in Proceedings of ACM SAC (2003), pp. 841–846
B. Waad, B.B. Atef, L. Mohamed, feature selection by rank aggregation and genetic algorithms, in Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (Vilamoura, Algarve, Portugal, 2013), pp. 74–81
L. Yan, C. Gui, W. Du, Q. Guo, An improved pagerank method based on genetic algorithm for web search, in Proceedings of Advanced Control Engineering and Information Science by Procedia Engineering 15 (Elsevier, 2011), pp. 2983–2987. https://doi.org/10.1016/j.proeng.2011.08.561
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kaur, P., Wang, GG., Singh, M., Singh, S. (2021). Rank Aggregation Using Moth Search for Web. In: Gupta, D., Khanna, A., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1165. Springer, Singapore. https://doi.org/10.1007/978-981-15-5113-0_5
Download citation
DOI: https://doi.org/10.1007/978-981-15-5113-0_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5112-3
Online ISBN: 978-981-15-5113-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)