An Method of Self-learning in Adaptive Text Information Filtering
With the growing popularity of Internet and development, the amount of information on the Internet is beyond people’s imagination. Information filtering is searching interesting information for user and shielding other useless information in dynamic information flow based on user’s need of information. In this background, information filtration comes into being and becomes a very important branch in information processing field. Because text information is one of the most important forms of Internet information, this article focuses on text filtering processing. This paper mainly searches how to build user template more precisely, the template learning algorithm in filtering processing and adaptive threshold in adaptive information filtering processing based on vector space model. Adaptive learning is fulfilled through genetic optimization of feedback information. Updating user templates by data gained by adaptive learning helps adaptive filtering. According to the experimental result, this method has shielded the information sparsely of the pseudo-relevance feedback and the misleading of the feature ambiguity effectively to improve the filtering quality of the adaptive information filtering system.
adaptive information filtering:user template feedback information:genetic optimization
Ning Hui Tan Ya-zhou Lv Zhi-long Wu Yue Cui Li-gang Wang Chun-hua
Computer Science and Technology College Harbin Engineering University.Harbin 150001.China School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China Computer Science and Technology College Harbin Engineering University.Harbin 150001.
国际会议
哈尔滨
英文
976-981
2010-01-08（万方平台首次上网日期，不代表论文的发表时间）