Open AccessMachine Learning as a New Search Engine Interface: An Overview
Author(s) -
Taposh Kumar Neogy,
Harish Paruchuri
Publication year - 2014
Publication title -
engineering international
Language(s) - English
Resource type - Journals
ISSN - 2409-3629
DOI - 10.18034/ei.v2i2.539
Subject(s) - search engine , world wide web , computer science , the internet , metasearch engine , web search engine , web page , spamdexing , interface (matter) , search analytics , web crawler , information retrieval , web search query , operating system , bubble , maximum bubble pressure method
The essence of a web page is an inherently predisposed issue, one that is built on behaviors, interests, and intelligence. There are relatively a ton of reasons web pages are critical to the new world, as the matter cannot be overemphasized. The meteoric growth of the internet is one of the most potent factors making it hard for search engines to provide actionable results. With classified directories, search engines store web pages. To store these pages, some of the engines rely on the expertise of real people. Most of them are enabled and classified using automated means but the human factor is dominant in their success. From experimental results, we can deduce that the most effective and critical way to automate web pages for search engines is via the integration of machine learning.