Evolving Information Filtering for PersonalizedInformation Service  

作　　者：田范江 李丛蓉 王鼎兴 

出　　处：《Journal of Computer Science & Technology》2001年第2期168-175,共8页计算机科学技术学报（英文版）

基　　金：he National '863' High-Tech Programme of China (No. 863-306- ZT01- 03- 1 ) ,IBM China Research Lab and Huawei Enterprise Fundi

摘　　要：Information filtering (IF) systems are important for personalized information service. However, most current IF systems suffer from low quality and long training time. In this paper, a refined evolving information filtering method is presented. This method describes user's information need from multi-aspects and improves filtering quality through a process like natural selection. Experimental result shows this method can shorten training time, improve filtering quality, and reduce the relevance between filtering results and training sequence.Information filtering (IF) systems are important for personalized information service. However, most current IF systems suffer from low quality and long training time. In this paper, a refined evolving information filtering method is presented. This method describes user's information need from multi-aspects and improves filtering quality through a process like natural selection. Experimental result shows this method can shorten training time, improve filtering quality, and reduce the relevance between filtering results and training sequence.

关 键 词：information filtering information processing personalized  information service 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]