Mining User'S Browsing History To Personalize Web Search

In today's world, search engines have become a very convenient method of searching and retrieving information. But this increasing use of search engines goes hand in hand with the ever-increasing data available on the internet. With such large number of websites available, it is essential to have these websites sorted in decreasing order of their relevance to the user's query for effective operation and retrieval of data. This paper explores various domains related to Computer Science and proposes a framework that seems the best fix to this problem. The proposed framework aims to maximize personalization of web search for each user by modeling the user profile at the user level and leveraging this information to rearrange the Search Engine Result Pages (SERP) and achieve the objectives. This framework also incorporates an adaptive model based on supervised learning, which records feedbacks to improve its performance over time. After testing the software, we could derive from the results that users could relate to the modified search result pages on deeper levels. It also marked a significant reduction in time and efforts incurred searching on Search Engine.