摘要
依据用户访问行为的连续性,引入本体技术对用户访问路径进行语义描述,生成语义路径图。提出基于语义路径的用户兴趣识别方法,生成用户兴趣本体,通过层次聚类法按兴趣本体相似度划分用户模式。利用协同推荐技术,进行显性需求和隐性需求的推荐,实现个性化知识服务。对比实验显示,该方法可提高用户兴趣识别的准确度和个性化知识服务的满意度。
For generating semantic path graph, it discusses the semantic description of the users' visiting path through ontology technology according to the continuity of users' visiting behavior. First, it proposes the users' interest recognition method based on semantic path, and generates the users' interest ontology. Then, it divides the users' mode in terms of interest ontology similarity through hierarchical clustering method. At last, for realizing personal knowledge service, it does the recommendation of visible requests and hidden requests by using cooperative recommendation. Experiment shows that the proposed method can increase the accuracy of users' interest recognition and improve the satisfaction of personalized knowledge service.
出处
《图书情报工作》
CSSCI
北大核心
2011年第15期106-109,共4页
Library and Information Service
基金
重庆市教委人文社科项目"基于语义路径分析的个性化知识服务研究"(项目编号:10SKP09)研究成果之一
关键词
语义路径
用户兴趣建模
协同推荐
个性化服务
semantic path user interest modeling cooperative recommendation personalized service