Published Paper Details:

Nowadays the number of users has increased in use of internet web access as well as the growth of data has also increased. This can be a hassle for users to find exactly relevant information from the Internet relatively quickly. The search process on the web is also inaccurate and it takes a lot of time to get results. Domain-specific web search engines contain information that is specific to the subject at hand. This domain-specific web search engine aims to improve accuracy and provide additional functionality. This makes it easy to connect young minds with start-ups that have turned out to be quite different from common web search engines. This proposed task uses a focused crawler that attempts to index only web pages that contain information about jobs, launch-related events, and news. It also uses contextual pseudo-relevance feedback using machine learning algorithms to get more relevant documents than regular search engines. This proposed task produces search results by reflecting feedback without human intervention. This task uses machine learning techniques to improve accuracy with domain-specific web search engines for better results