Abstract: An important characteristic of next generation search engine is personalization. Personalized information retrieval (PIR) focuses on users. It captures users’ interest in different kinds (explicit, implicit interest and interest of similar users). These information of users are integrated and used to improve the result of information retrieval system. Personalized information retrieval can grasp the users’ retrieval intention and find personalized results. The authors propose the new interest detection task, which identifies the queries containing users’ new retrieval interest by the change of retrieval object. Simultaneously, by using and improving the TextTiling algorithm, the retrieval system is enabled to automatically choose the appropriate dynamic threshold and detect the change of users’ interest. The retrieval information and labeled answers of users are used to establish the experimental dataset. The evaluation matrix includes false alarm rate, miss alarm rate, and cost of detection. In the experiment of personalized information retrieval system, the improved TextTiling algorithm improves the new interest detection system by 16.4%. What’s more, the new interest detection task improves the performance of the personalized information retrieval system is by 3.8%. The experiment shows that mining users’ interest with this method can decrease the false information in users’ models and improve the result of precision of users’ interest detection.