Tuzhi Pilots Subvert the Traditional Customized Future Education
The rapid development of Internet technology has promoted the rapid popularization of online education platforms and created new opportunities for personalized smart education. The academic and industrial sectors are constantly increasing their attention to recommendation algorithms. Although these algorithms have achieved remarkable results in the e-commerce field, they still face problems such as insufficient mining of implicit interactive data, insufficient knowledge guidance, and lack of practical recommendation systems in online education. To solve these challenges, we have built a smart course recommendation system for industrial applications. By mapping the implicit interaction data between users and courses into heterogeneous diagrams, and integrating course knowledge information to deeply analyze the complex connections between users and courses, we have designed an efficient pipeline that includes preprocessing, recall, offline sorting, online recommendation and results, which not only achieves rapid response to recommendation requests, but also effectively alleviates practical application problems such as cold start. Finally, comparative experiments based on real teaching platform data verified that the system has significant advantages in performance and application.
