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中文摘要：
个性化信息检索可以根据用户的检索兴趣返回个性化的检索结果。提出了用户新兴趣发现子任务，根据用户检索对象的变化识别包含新检索兴趣的查询。同时，引入TextTiling方法并对其进行改进，使系统可以自动选择合适的动态阈值并准确发现用户检索兴趣的转移。在构建的标准评测集上的实验结果表明，改进的TextTiling方法使得用户新兴趣发现系统性能提高了16.4％，而且此子任务使得最终的个性化检索系统的性能提高了3.8％。
英文摘要：
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.
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A new query expansion approach using collocation relationships in language models for information re
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