关键词: 语义关联, 商品推荐, 本体, 关联数据

Abstract: In view of the fact that background data source of recommended objects is single and commodity recommendation lacks diversity in traditional recommendation systems, this paper puts forward a diversified commodity recommendation algorithm. The relationship between objects is divided into three categories, which is respectively complementary correlation, similarity correlation and scenario correlation. It analyzes these correlations between customer interest ontology and recommended commodities, as a result, deduces commodity recommendation list of the customer. The experimental results show that compared with traditional recommendation method only to consider similarity correlation, the algorithm enriches the type of recommended commodities, optimizes recommendation list and further meets consumers’ demand for complementary or scene correlation commodities.

Key words: semantic association, commodity recommendation, ontology, relational data