Open AccessOptimizing webpage relevancy using page ranking and content based ranking
Author(s) -
J Satish Babu,
T. Ravi Kumar,
Shahina Bano
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.7.12220
Subject(s) - web page , computer science , information retrieval , heuristics , ranking (information retrieval) , set (abstract data type) , web mining , static web page , backlink , rank (graph theory) , world wide web , data mining , web development , mathematics , combinatorics , programming language , operating system
Systems for web information mining can be isolated into a few classifications as indicated by a sort of mined data and objectives that specif-ic classifications set: 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, we call it Page Content Rank (PCR) in the paper, 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 which the page contains. The significance of a term is determined concern-ing 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 reacted 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.