Information Retrieval Based on the Sequential Hypertext Induced Topic Search in Web Pages

The World Wide Web is widely used as a global standard for collecting information. Millions of Internet pages provide information on the existence of users worldwide. To extract the relevant web page, a search engine application is applied to the online search because of the important information through search queries entered by the user or page rank modules. The size and complexity of the World Wide Web are growing. That’s why it needs to be improved as the pages of information are more important in terms of better user experience who enters a query in a search engine. Website traffic analysis and network users in the development of Internet usage are critical in the world. The way of growth point to extract intelligence information resources knowledge and develop a variety of client and server-side tools. The traditional Web search engines take a long time to browse hundreds of thousands of users to return search results. Online library search engines and other large file libraries (such as customer support database product specification database, News Release Archives, News archive of articles, etc.) become difficult and expensive to file in manually. To overcome the problem, in this work, Sequential Hypertext Induced Topic Search (SHITS) method for web search is proposed and the role of web association is to provide web search engine’s different implementations of relevant results. The proposed method is using web search based on user behavior analysis using the Information Retrieval (IR). The web-based database of semantically related concepts is connected in Uniform Resource Locators (URL). It also uses the SHITS algorithm to extract useful keyword frequency from a huge set of words on each page and keywords. The proposed algorithm is used in many optimizations and the user’s useful results are obtained.