Title: Web page ranking using ant colony optimisation and genetic algorithm for effective information retrieval

Authors: Suruchi Chawla

Addresses: Department of Computer Science, Shaheed Rajguru College of Applied Science, University of Delhi, Vasundhara Enclave, India

Abstract: In this paper novel method is proposed to generate optimal ranking based on clustered query sessions using hybridisation of ant colony optimisation (ACO) and genetic algorithm (GA) for effective information retrieval. The advantage of using ACO with GA for web page ranking is that both complement each other in optimisation and overcome local minima problem therefore it generates the optimal ranking of clicked URLs. The optimal ranking of web pages (clicked URLs) when used for recommendations retrieve more and more relevant documents up in ranking and improve the precision of search results. The recommendation of optimal ranking of clicked URLs continues during web search for effective personalisation of user search goal. Experiment was conducted on the data set captured in three domains and results were analysed statistically to confirm the improvement of precision of search results using the proposed method.

Keywords: information retrieval; search engines; ant colony optimisation; ACO; genetic algorithms; information scent; clustering; web page ranking; clustered query sessions; clicked URLs; recommendation systems; relevant documents; search precision; search results; recommender systems; web search; personalisation; metaheuristics; swarm intelligence.

DOI: 10.1504/IJSI.2017.082397

International Journal of Swarm Intelligence, 2017 Vol.3 No.1, pp.58 - 76

Received: 18 Jan 2016
Accepted: 04 Oct 2016
Published online: 22 Feb 2017 *

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