Optimizing webpage relevancy using page ranking and content based ranking.

Babu, J. Satish; Kumar, T. Ravi; Bano, Shahana

Abstract

Systems for web information mining can be isolated into a few classifications, depending on the type of target data and the purposes of the activity: Web structure mining; Web utilization mining; and Web Content Mining. This paper proposes another Web Content Mining system for page significance positioning, taking into account the page content investigation. The strategy, Page Content Rank (PCR), consolidates various heuristics that appear to be critical for breaking down the substance of Web pages. The page significance is resolved on the base of the significance of terms that the page contains. The significance of a term is determined concerning a given inquiry "q", and it depends on its measurable and linguistic elements. As a source set of pages for mining, we utilize an arrangement of pages retrieved by a web search tool to the question "q". PCR utilizes a neural system as its inward order structure. We depict a usage of the proposed strategy and an examination of its outcomes with the other existing characterization framework - page rank algorithm.

Citation

BABU, J.S., KUMAR, T.R. and BANO, S. 2018. Optimizing webpage relevancy using page ranking and content based ranking. International journal of engineering and technology (Science Publishing Corporation) [online], 7(2.7): proceedings of the 2018 International conference on Internet of Things (IoT) and cyber security (ICICS 2018), 22-23 March 2018, Vijayawada, India, pages 1025-1029. Available from: https://doi.org/10.14419/ijet.v7i2.7.12220

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2018 International conference on Internet of Things (IoT) and cyber security (ICICS 2018)
• Acceptance Date: Mar 1, 2018
• Online Publication Date: Mar 18, 2018
• Publication Date: Mar 18, 2018
• Deposit Date: Sep 21, 2023
• Publicly Available Date: Sep 21, 2023
• Journal: International journal of engineering and technology (Science Publishing Corporation)
• Electronic ISSN: 2227-524X
• Publisher: Science Publishing Corporation
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 2.7
• Pages: 1025-1029
• DOI: https://doi.org/10.14419/ijet.v7i2.7.12220
• Keywords: Search engine optimisation; Search engine result ranking; Webpage rankings; Webpage content analysis; Text and data mining
• Public URL: https://rgu-repository.worktribe.com/output/2064128