Personalized Web Search Using Probabilistic Query Expansion

The Web consists 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 systems aim to provide relevant results to users by taking user interests into account. In this paper, we proposed a personalized Web search system implemented at proxy which adapts to user interests implicitly by constructing user profile with the help of collaborative filtering. A user profile essentially contains probabilistic correlations between query terms and document terms which is used for providing personalized search results. Experimental results show that our proposed personalized Web search system is both effective and efficient.