Research on a personalised education recommendation system based on deep learning: from learning data to customised learning paths

With the rapid application of artificial intelligence, especially deep learning technology, in the field of education, personalised education recommendation systems have become an important means of enhancing learning outcomes and teaching quality. The deep learning-based personalised education recommendation system can not only effectively handle large-scale, multi-dimensional student data, but also intelligently generate customised learning paths based on students' learning status and behaviour. However, there are still many areas in the optimisation of learning pathways and the enhancement of student learning outcomes that remain to be fully explored. This article aims to propose cutting-edge topics that have not been sufficiently researched from the perspective of personalised recommendation systems, including personalised emotional modelling for students, the recommendation of long-tail learning resources, and the impact of multi-source data fusion on the effectiveness of personalised recommendations. To this end, this article also designs a practical experimental plan aimed at verifying the research value of these emerging topics and demonstrating the broad prospects and practical significance of deep learning-based recommendation systems in the field of education.