Enhanced Reinforcement Algorithm for Topic Categorization Using Machine Learning Method

Abstract

Information seekers rely heavily on search engines to extract relevant information because of the Internet's exponential development in users and traffic. The availability of a vast amount of textual, audio, video, and other content has expanded search engines' duty. Users of the Internet can obtain pertinent information about their query from the search engine by using factors like content and link structure. It does not, however, imply that the information is accurate. The link structure of web sites is analyzed using Web Structure Mining (WSM), and their content is analyzed using Web Content Mining (WCM), which determines how well the ranking module performs.A multitude of content-based recommender systems are currently in use, and they are well-researched in both text acquisition and filtering. These systems recommend documents based on text analysis. The management information system can benefit from Web Content Ming technology. Web content mining is the process of extracting or mining knowledge or useful information from web pages. The purpose of this work is to investigate web content extraction technology enhanced Reinforcement algorithm which anticipates user interest by analyzing the page according to user view related topic.This ERIM procedure involves locating web sites linked to user queries and using hyperlinks to locate a collection of related web pages and find the topic categorization using machine learning method.