摘要
该文对如何满足不同兴趣用户的查询需求进行了研究,提出了一种基于用户偏好分析的查询优化方法。该方法将用户对网页的偏好转化为对本体知识库中实例的偏好;分析本体实例之间的语义关联,发现隐含的用户偏好;综合用户偏好历史,建立用户当前状态下偏好的数学模型,以预测用户对网页的关注程度。实现了相应的原型系统,实验结果表明,相对于传统的个性化搜索技术,该文提出的方法能更有效地获取用户偏好,适应用户偏好的变化,提高搜索引擎查询的准确率。
User profiles, descriptions of user interests, can be used by search engines to provide personalized search results. A query optimization method based on user profiles reasoning is presented. This method creates user profiles by classifying users＇ interests into instances in an ontology knowledge base, and then propagates user preferences to find users＇ latent interests by analyzing the semantic association among the ontology instances. It integrates users＇ current and history preferences to process the search results. A prototype system is implemented and the experimental results show that users＇ latent preferences can be learned accurately and personalized search based on user preference yields significant improvements over the origin results.
出处
《电子与信息学报》
EI
CSCD
北大核心
2008年第1期33-37,共5页
Journal of Electronics & Information Technology
基金
国家自然科学基金重点项目(60432010)
国家973计划项目(2003CB314806)资助课题
关键词
网络搜索
个性化
本体
语义关联
Web search
Personalization
Ontology
Semantic association
