Optimization of MOOC resource recommendation algorithm for art studies based on the google dataflow architecture

As online education continues to rapidly grow, MOOCs (Massive Open Online Courses) platforms have become a crucial part of arts education. However, the existing arts MOOCs recommendation systems face several challenges, including data processing delays, limited scalability, and inadequate real-time performance, which negatively impact user experience and learning efficiency. Existing studies largely depend on traditional algorithms such as collaborative filtering and content-based recommendations, which are prone to performance bottlenecks when handling large datasets and fail to fully meet the demands for personalized and efficient learning. To address these challenges, this paper proposes an optimization strategy for arts MOOCs resource recommendation algorithms based on the Google Dataflow architecture, aimed at enhancing user satisfaction and learning efficiency. This approach utilizes the robust real-time computing and scalability features of Google Dataflow to design and implement a novel recommendation algorithm capable of efficiently processing large-scale data streams and continuously updating user preference models. By integrating the benefits of collaborative filtering with content-based recommendations, the proposed system achieves significant improvements in recommendation accuracy and personalization. Experimental results demonstrate that the recommendation system based on Google Dataflow architecture outperforms traditional methods in processing speed, accuracy, and user satisfaction, showcasing its potential for application in arts MOOCs platforms and contributing to the advancement of personalized learning experiences.