Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering

Abstract

This study aims to solve the problem of limited learning efficiency caused by information overload and resource diversity in online course learning. We adopt a recommendation algorithm that combines knowledge graph and collaborative filtering, aiming to provide an application that can meet users’ personalized learning needs and consider the semantic information of learning resources. In addition, this article collects and models implicit data in online courses and compares the impact of video and text learning resources on user learning needs under different weights in order to deeply understand the different contributions of video and text learning resources to meeting learning needs. The experimental results show that the video high-weight experimental group performs better than the text high-weight experimental group; students tend to prefer video resources. This experiment can help students cope with the challenges brought by numerous types of learning resources and provide personalized and high-quality learning experiences for learners. At the same time, adjusting and innovating teaching models for teachers has great reference value.