| 英文摘要 |
Query Expansion was designed to overcome the barren query words issued by user and has been applied in many commercial products. This treatment tries to expand query words to identify users'real requirement based on semantic computation. It may be critical to deal with the problem of information overloading and diminish the using threshold, however the modern retrieval systems usually lack user modeling and are not adaptive to individual users, resulting in inherently non-optimal retrieval performance. In this study, we propose the LLSF method based on each individual search history to automatically generate specific personalized profile matrix. By which to generate context-based expanded query words. Considering the accuracy of retrieving performance, we process query words re-weighting algorithm to achieve this goal. Finally, the documents list is ranked by the way of stressed density distribution modeling. And the experimental result shows that our framework corresponds to personalization and the performance is very promising. |
本站僅提供期刊文獻檢索。
