Multimedia content recommendation algorithm based on behavior and knowledge feature embedding

Abstract

As internet information technology continues to advance, individuals are increasingly encountering and managing a vast volume of data and information. A large and complex amount of information hinders the effective transmission of valuable information, making it difficult to find multimedia content of interest in the vastness of the internet. As the volume of multimedia content rapidly grows, personalized recommendation algorithms play a crucial role in matching relevant content to users. Knowledge graphs, due to their powerful organizational and relationship processing capabilities, are commonly applied in intelligent search engines and recommendation systems. This article focuses on the effective utilization of semantic association information in knowledge graphs for multimedia content recommendation. Two main areas of research are conducted. In this article, two novel approaches are presented. To begin with, the primary objective is to improve the learning of knowledge feature representation. This is achieved by introducing a model based on self-attention, which effectively captures the diverse significance of triplets in determining the semantics of entities. This leads to improved quality of knowledge feature representation, thereby serving as valuable auxiliary information for multimedia content recommendation systems. Secondly, the article addresses the integration of knowledge graphs in multimedia content recommendation applications. This paper proposes a content recommendation algorithm that integrates a combined embedding of behavior and knowledge features. By leveraging past preferences and utilizing the semantic structure of knowledge graphs, this algorithm provides a comprehensive exploration of user interests and hobbies. Ultimately, this conducts extensive experiments to assess the effectiveness and performance of the proposed algorithms. The results validate the feasibility and efficacy of these algorithms in enhancing multimedia content recommendation systems.