摘要
为改善用户的Web页面访问行为、提高访问效率,设计了一种基于贝叶斯网络的网页推荐模型及推荐算法。通过收集和分析服务器中的描述文件和日志文件,利用Bayesian网络分析页面间的依赖关系,构建了基于贝叶斯网络的网页推荐模型并产生推荐集。通过在Microsoft公司提供的网络日志数据集上做的实验,可以获得超过80%的准确率和覆盖率。理论分析和实验结果表明:算法能够在线实时向用户做出个性化的推荐,与已有的推荐算法相比,算法能较快地给出推荐集,并且可以获得更高的准确率和覆盖率。
A model based on the Bayesian network and corresponding algorithm for web page recommendation were presented to improve users＇ behavior on browsing web pages and enhance visiting efficiency.The model was constructed by collecting and analyzing description files and log files in the servers and using the Bayesian network to analyze the dependence among the web pages.Then the model was built and the recommendation set was generated.By conducting experiments on the network log data sets provided by Microsoft Company,the precision and coverage obtained were both higher than 80%.The results of theoretical analysis and experiments indicated that the algorithm could make personalized recommendation for users in real time online.Compared with other existing algorithms,this algorithm could give the recommendation set more quickly with higher precision and coverage.
出处
《山东大学学报（工学版）》
CAS
北大核心
2011年第4期137-142,共6页
Journal of Shandong University（Engineering Science）
基金
国家自然科学基金面上项目(61073193)
安徽省自然科学基金资助项目(11040606M152)
安徽省高校省级自然科学研究项目(KJ2011ZD06)
关键词
数据挖掘
个性化推荐
协同过滤
贝叶斯网络
Data mining
personalized recommendation
collaborative filtering
Bayesian network
