OHR:一种基于本体的个性化混合服务推荐模型

随着网络信息量的日益增加,为用户提供个性化服务是一种趋势。该文通过建立一个通用的服务本体模型,将项目集合划分到多个服务子类中,经过概率计算得到用户的兴趣分布,并在此基础上提出了一个结合内容过滤和项目协同过滤的个性化混合服务推荐模型(OHR)。实验结果表明了该模型在服务推荐上具有较高的准确率和发现用户新兴趣的能力。

OHR:A Hybrid Personalized Recommendation Model Based on Ontology

With the dramatic increase of information available on the Internet, it is obviously a trend to provide users with personalized service. In this paper, through building a generalized service model based on ontology, the Items are classified into service sub-category. and the probability distribution of the users′ interests are calculated. On the basis of the combination of Content Filtering and Item-based Collaborative Filtering, an new ontology-based hybrid personalized recommendation model(OHR) is put forward. The experimental results show that OHR provides the better recommendation results than traditional collaborative filtering algorithms, as well as the better ability to discover the users′ new interests.