Personalized Query Expansion based on phrases semantic similarity

As a result of the rapid advancements in Information Technology, Information Retrieval on Internet is gaining importance, day by day. The web comprises of huge amount of data and search engines provide an efficient way to help navigate the web and get the relevant information. General search engines, however, return query results without considering user´s intention behind the query. Personalized Web search is carried out for information retrieval for each user incorporating his/her interests. This paper presents a Personalized Query Expansion system which aims to provide relevant results by taking user interests into account. User profile is generated without user interaction i.e. automatically monitoring users browsing habits. The proposed method tries to construct query phrases considering user´s real requirement based on semantic similarity which will further help in retrieving efficient search results. Experimental results show that the proposed algorithm can provide more precision than the traditional query expansion methods and hence reduces the computational time.