Combination of Knowledge Graphs to Make Personalized Recommendations for English Teaching Content

The abundance of online resources provides students with richer and more diverse learning resources, but problems such as resource fragmentation, scattered learning content, and lack of personalised learning paths. Therefore, this article combines the use of knowledge graphing to make personalised recommendations for English learning content to improve students' efficiency in absorbing English learning knowledge. To meet the needs of students at different levels, this article adopts an assistive teaching system based on intelligent recommendations technology to provide each student with personalized learning resources through frequent item set mining and personalized recommendations. This article first points out that recommender systems are required to meet students' needs to find the learning materials they want. It then investigates how to build a website for daily use by teachers and students. Secondly, it specifically addresses the ideas of classroom teaching transformation and the establishment of domain knowledge graphs. Finally, this article selects 50 college students and adopts the knowledge graph support system for personalised recommendation learning at different stages. The experimental results show that the enhancement of each student's academic performance is approximately between 10% and 20%. The average relevance score of the recommended resources is 4.41 points (out of 5 points), which indicates that most of the recommended learning Resources can highly meet the actual needs of the students, and the students are generally pleased with the system.