摘要
传统信息过滤模型很难描述对信息过滤结果产生影响的各种因素 ,如质量、内容、用户偏好之间复杂的关系 ,也无合适的方法让用户将知识加入到信息过滤系统中 .因此 ,提出了基于贝叶斯网络的信息过滤模型 BMIF(Bayesian m odel of inform ation filtering) .BMIF是贝叶斯网络的简化 ,它描述了信息过滤的基本结构 ,提供了 6种节点用于描述影响信息过滤的事件之间的关系 .在此基础上 ,提供了 BMIF的各种使用方法 ,包括将传统方法使用BMIF描述 ,将词法知识用 BMIF表示 ,以及将自动学习与手动交互结合 。
Traditional models of content based information filtering are clumsy to describe the complex relationships of events that affect filtering process. Furthermore, it is difficult for users to interact with the filtering system. To address the above problems, BMIF-an information filtering model founded on Bayesian network is proposed in this paper. It firstly outlines the relationship of features, interests, queries and other main elements in information filtering with a simplified Bayesian network, and then provides six elementary nodes to characterize the relationships in specific conditions. Some use cases of BMIF are presented as well, which includes: ① Describing traditional models with BMIF; ② Adding lexical knowledge to BMIF; ③ Mixing learning with interaction, and combining content based filtering with collaborative filtering.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2002年第12期1564-1571,共8页
Journal of Computer Research and Development
基金
国家重点基础研究发展规划基金 (G19990 3 5 80 7)
国家自然科学重点基金 (6983 3 0 3 0 )资助
关键词
贝叶斯网络
信息过滤模型
信息处理
专家系统
information filtering, Bayesian network, collaborative filtering, content based filtering
