Web search engines are critical for overcoming information overload. A major deficiency of existing retrieval systems is that they generally lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. For example, a Programmer and Geologist may use the same word “python” to search for different information, but the current search systems would return the same results. That is how it is essential to model a user profile that is useful to re-rank result based on user’s current interest.The proposed framework presents a novel approaches to personalize web search through modeling user profile and reformulate user’s query by analyzing his/her previous search history, click, long term interest as well as short term interest.