文档介绍：北京大学学报(自然科学版)
Acta Scientiarum Naturalium Universitatis Pekinensis
doi: -
基于本体结构的新闻个性化推荐
饶俊阳贾爱霞冯岩松†赵东岩
北京大学计算机科学技术研究所, 北京 100871; †通信作者, E-mail: ******@pku.
摘要为了更好地对新闻和用户进行建模, 将语义相似度模型引入到基于内容的推荐系统中, 挖掘两者之间
的语义关联。提出一种基于本体结构的相似度模型(OBSM), 利用在线百科构建的本体结构, 计算新闻和用户
之间的语义相似度。为了降低本体结构上噪音数据对推荐效果带来的影响, 提出 X-Ontology 聚类算法对本体
结构进行清理, 并衍生出 OBSM 的升级模型 X-OBSM。中文和英文实验表明, OBSM 和 X-OBSM 比基准模型
具有更好的推荐效果, 尤其是 X-OBSM 对本体结构进行清理后, 具有比 OBSM 更高的计算效率。
关键词个性化推荐;用户建模;本体结构
中图分类号 TP18
Ontology-Based News Personalized mendation
RAO Junyang, JIA Aixia, FENG Yansong†, ZHAO Dongyan
Institute puter Science and Technology, Peking University, Beijing 100871;
† Corresponding author, E-mail: ******@pku.
Abstract The authors concentrate on exploiting the background knowledge to address the semantic analysis in
content-based filtering. An Ontology Based Similarity Model (OBSM) is proposed to calculate the news-user
similarity through collaboratively built ontological structures. In order to deal with the noisy nature of these
coarse-grained structures, an ontology based clustering model is introduced into the framework, called X-OBSM,
which clusters concepts of a user profile on a coarse-grained ontology. Experimental results show that both OBSM
and X-OBSM outperform the baselines by a large margin; specifically, X-OBSM perfor