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. Experiment results show that both OBSM and X-OBSM outperform the baselines by a large margin, specifically, X-OBSM performs better than OBSM in both quality and efficiency.

Key words: personalized recommendation, user profiling, ontology

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, 尤其是对本体结构进行清理后, X-OBSM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