Uncovering user’s search patterns to personalise 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. We have proposed a new system to provide personalized web search according to the user’s internet surfing patterns. The system extracts the user’s history and scrapes the web pages’ content (title, keywords, headings, sub-headings, meta tags). These documents are then clustered using Word2Vec model and Latent Semantic Indexing to give better results. User’s search query is mapped to the profile and an appropriate cluster is selected. The SERP returned by the search engine is mapped to the selected cluster to find the similarity index. A linear regression model is used to assign the final score which takes the regency, frequency, popularity and user’s feedback along with the similarity measure to re-rank the SERP.