A Bio-inspired Modified PSO Strategy for Effective Web Information Retrieval using RCV1 Datasets
Ramya C1, Shreedhara K S2

1Ramya C, Research Scholar, Department of Studies in CS&E, UBDTCE, VTU, Davanagere, Karnataka, India.
2Dr. Shreedhara K S, Professor, Department of Studies in CS&E, UBDTCE, VTU, Davanagere, Karnataka, India.

Manuscript received on 03 July 2019 | Revised Manuscript received on 07 July 2019 | Manuscript published on 30 August 2019 | PP: 779-785 | Volume-8 Issue-10, August 2019 | Retrieval Number: J89030881019/2019©BEIESP | DOI: 10.35940/ijitee.J8903.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Information retrieval is a key technology in accessing the vast amount of data present on today’s World Wide Web. Numerous challenges arise at various stages of information retrieval from the web, such as missing of plenteous relevant documents, static user queries, ever changing and tremendous amount of document collection and so forth. Therefore, more powerful strategies are required to search for relevant documents. In this paper, a PSO methodology is proposed which is hybridized with Simulated Annealing with the aim of optimizing Web Information Retrieval (WIR) process. Hybridized PSO has a high impact on reducing the query response time of the system and hence subsidizes the system efficiency. A novel similarity measure called SMDR acts as a fitness function in the hybridized PSO-SA algorithm. Evaluations measures such as accuracy, MRR, MAP, DCG, IDCG, F-measure and specificity are used to measure the effectiveness of the proposed system and to compare it with existing system as well. Ultimately, experiments are extensively carried out on a huge RCV1 collections. Achieved precision-recall rates demonstrate the considerably improved effectiveness of the proposed system than that of existing one.
Keywords: Information Retrieval Systems, Web Information Retrieval, Particle Swarm Optimization, Similarity Functions, Documents Collection.
Scope of the Article: Information Retrieval