A study on the personalized recommendation of intelligent learning system based on user feature model analysis

Abstract

In the digital intelligence era, users have higher requirements for machine aided learning environment experience. How to provide personalized learning support services based on users' different characteristics has become a hot topic for researchers. Intelligent learning system (ILS) is a learning support environment that can dynamically diagnose users' different learning needs and then provide personalized services. However, the current research on intelligent learning systems is still in the exploratory stage, and the research results need to be improved in the aspect of intelligent recommendation effect. Based on this, this paper will further explore the personalized recommendation technology solution of intelligent learning system on the basis of the analysis of relevant case results. In order to improve the recommendation accuracy of intelligent learning system, we will focus on the analysis of the system architecture, feature model construction method and recommendation process from the perspective of user feature model. The simulation experiment analysis shows that the research results have certain advantages in the personalized recommendation effect, which can dynamically provide the current user with a suitable personalized learning path to meet the user's learning needs.