基于Web挖掘技术的用户兴趣本体学习研究

Research on the User Interest Ontology Learning Based on Web Mining Technology

目前针对基于关键词的用户模型不能从语义上表达用户需求真正内涵,基于领域本体的用户模型多数忽略研究概念间非分类关系和语义应用环境较分散等缺陷。本文提出一种循环式的基于Web挖掘技术的用户兴趣本体学习模型,即综合应用统计分析、关联规则和聚类分析等技术进行电子商务领域用户兴趣概念及概念间分类与非分类关系学习,面对用户兴趣的迅速变化,还提出一种传递激活方法来实时更新本体或重新进行本体学习,以不断提高该本体的质量。经验证,基于该本体的用户模型在文本过滤等应用中能较上述两种用户模型满足用户个性化服务需求。

At present,keywords based user model can not really express the semantic content of users needs,and domain ontology based user model always ignore to research the non-taxonomic relations of concepts and make the semantic applications environment distributed.Faced with those problems,this paper presents a cyclic user interest ontology learning model based on web mining technology which learns concepts and their taxonomic and non-taxonomic relations in ecommerce to capture users＇ interests comprehensively using techniques of statistical analysis,association rules and clustering analysis.When facing the rapid change of users＇ interests,it presents a spreading activation method to update or relearn user interest ontology,which aims to improve the quality of this ontology continuously.By experiencing,the user profile based on this ontology can better meet the needs of users compared with other user profile.