Information Recommendation Model Based on Knowledge Graph in Personalized Context

Abstract

With the rapid development of the Internet, information overload makes it impossible for users to quickly find interesting content in front of massive information. Therefore, personalized recommendation method has wide prospect. In recent decades, collaborative filtering has become a mainstream recommendation technology. In view of the existing research on information recommendation and the lack of in-depth analysis of user personalized scenarios, this paper proposes a personalized scenario recommendation model in mobile e-commerce environment based on knowledge graph. Its main framework and specific content are given in detail. An information recommendation algorithm is proposed based on knowledge graph and collaborative filtering. This algorithm combines the recommendation information in knowledge graph with collaborative filtering method. It adopts alternate learning mode and use the semantic information extracted from knowledge map to make up for the defect of item-based collaborative filtering method. Simulation results demonstrate that the proposed algorithm can improve the quality of personalized recommendation.