Improved content recommendation algorithm integrating semantic information

Content-based recommendation technology is widely used in the field of e-commerce and education because of its intuitive and easy to explain advantages. However, due to the congenital defect of insufficient semantic analysis of TF-IDF vector space model, the traditional content-based recommendation technology has the problem of insufficient semantic analysis in item modeling, fails to consider the role of semantic information in knowledge expression and similarity calculation, and is not accurate enough in calculating item content similarity. The items with semantic relevance in content can not be well mined. The research goal of this paper is to improve the semantic analysis ability of the traditional content-based recommendation algorithm by integrating semantic information with TF-IDF vector space model for item modeling and similarity calculation and proposed an improved content recommendation algorithm integrating semantic information. In order to prove the effectiveness of the proposed method, several groups of experiments are carried out. The experiments results showed that the overall performance of the proposed algorithm in this paper is the best and relatively stable. This verified the validity of our method.

Automated summary

This paper addresses the deficiency of traditional content-based recommendation technology in semantic analysis and proposes an improved recommendation algorithm that integrates semantic information with the TF-IDF vector space model. Experimental results demonstrate the effectiveness and stability of the proposed method.