Abstract: The classic recommendation system is mainly based on the users for the project evaluation or the keywords similarity between the user and the item for recommendation, there is a low degree of information structure, lack of semantic and other issues. To solve these problems, this paper proposes an ontology-based recommender system model. This model uses OWL language describe the user and project information by introducing ontology to bear more semantic information and improve the degree of information structure. In the recommendation process, the results of analyzing user behavior information by rules is considered for improve the quality of recommendation. Experimental results show that the model has better effect in degree of information structure and semantics. It can effectively improve the recall and precision rates by using this model for recommendation.

Key words: ontology, collaborative filtering, content-based recommendation, hybrid recommendation, Ontology Web Language(OWL), recommendation system

摘要： 经典推荐系统主要根据用户对项目的评价或者用户与项目之间的关键字相似度进行推荐,存在信息结构化程度低、语义缺乏、信息利用不充分等问题。为此,提出一种基于本体的推荐系统模型。将本体引入到推荐系统中,使用OWL 语言对用户和项目信息进行描述,使用户和项目具有语义信息的同时,提高信息的结构化描述水平。在推荐过程中,通过规则分析用户行为信息并综合考虑以提高模型的推荐质量。实验结果证明,与传统推荐模型相比,该模型在信息结构化水平、语义描述等方面具有优势。采用该模型为用户推荐项目能够有效提高推荐的召回率和准确率。

关键词: 本体, 协同过滤, 基于内容推荐, 混合推荐, Web 本体语言, 推荐系统

CLC Number: 

• TP39