基于层次聚类的自适应信息过滤学习算法  

Learning Algorithm of Adaptive Information Filtering Based on Hierarchy Clustering

作　　者：洪宇[1] 张宇[1] 刘挺[1] 郑伟[1] 龚诚[1] 李生[1] 

机构地区：[1]哈尔滨工业大学计算机科学与技术学院信息检索实验室,黑龙江哈尔滨150001

出　　处：《中文信息学报》2007年第3期47-53,共7页Journal of Chinese Information Processing

基　　金：国家自然科学基金资助项目(60435020;60575042;60503072)

摘　　要：本文采用一种基于层次聚类的自适应学习策略,从系统反馈的信息流中,动态提取一类最优信息的质心更新用户模型,有效屏蔽了阈值失真和初始信息稀疏造成的大量反馈噪声,并且能够近似模仿人工反馈,完善自适应学习机制的智能性。This paper adopts an adaptive learning algorithm based on hierarchy clustering to update user profile, which continuously abstract the cancroids of one class of optimum information from the feedback flow of system, which effectively shield the learning process from plenty of feedback noises produced by distorted threshold and sparseness of initial information, which also can imitate artificial feedback approximately to perfect the intelligence of adaptive learning mechanism.

关 键 词：计算机应用 中文信息处理 自适应信息过滤 用户模型 相关反馈 阈值 层次聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]