A new technology of query expansion based on new user interest model

The methods of query expansion have been studied for a long time- with debatable success in many instances which is one most important technology to finish personalized information. In this paper we propose a probabilistic query expansion based on a new user interest model which is constructed and updated automatically. We address the two important issues with query expansion, the selection and the weighting of additional search terms. In contrast to earlier methods, our model deals with the weight of those terms that have not been visited for a long time which can ensure our queries are expanded by those terms that users are interested in mostly during this period rather than selected terms randomly. Our experiments show that this kind of query expansion has more precise results than others.