Research on Bayesian Network Based User's Interest Model  

作　　者：ZHANG Weifeng XU Baowen CUI Zifeng XU Lei 

机构地区：[1]Department of Computer Science and Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, Jiangsu,China [2]School of Computer Science and Engineering, Southeast University, Nanjing 210096, Jiangsu, China [3]Jiangsu Institute of Software Quality, Nanjing 210096, Jiangsu, China

出　　处：《Wuhan University Journal of Natural Sciences》2007年第5期809-813,共5页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (60503020, 60503033, 60373066, 60403016);Opening Foundation of Jiangsu Key Laboratory of Computer Information Processing Technology in Soochow University

摘　　要：It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing users＇ interest models and some basic questions of users＇ interest （representation, derivation and identification of users＇ interest）, a Bayesian network based users＇ interest model is given. In this model, the users＇ interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users＇ interested and not interested documents are used to train the Bayesian network. Compared to the simple model, this model has the following advantages like small space requirements, simple reasoning method and high recognition rate. The experiment result shows this model can more appropriately reflect the user＇s interest, and has higher performance and good usability.It has very realistic significance for improving the quality of users＇ accessing information to filter and selectively retrieve the large number of information on the Internet. On the basis of analyzing the existing users＇ interest models and some basic questions of users＇ interest （representation, derivation and identification of users＇ interest）, a Bayesian network based users＇ interest model is given. In this model, the users＇ interest reduction algorithm based on Markov Blanket model is used to reduce the interest noise, and then users＇ interested and not interested documents are used to train the Bayesian network. Compared to the simple model, this model has the following advantages like small space requirements, simple reasoning method and high recognition rate. The experiment result shows this model can more appropriately reflect the user＇s interest, and has higher performance and good usability.

关 键 词：Bayesian network interest model feature selection 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]