With the rapid development of big data technology, personalized education recommendation systems have become an increasingly important means to improve the quality and efficiency of education. However, current personalized recommendation systems often suffer from issues such as insufficient accuracy of recommendation results and incomplete coverage of recommended resources. In response to these issues, this article proposes a personalized recommendation system for big data technology education based on the multi-objective particle swarm optimization (MOPSO) algorithm. This framework consists of data collection, preprocessing, feature extraction, model training, and recommendation generation. In the preprocessing stage, hierarchical clustering and feature selection are used to improve data quality. Introducing the MOPSO algorithm in the model training phase for collaborative optimization of multiple objectives, such as accuracy and diversity of recommendation systems. In the recommendation generation stage, personalized results are generated based on user behavior and preferences. The experimental results show that the system is significantly better than traditional methods on real educational datasets, with a recommendation accuracy improvement of about 10% (from 0.72 to 0.79) and a recommendation diversity improvement of about 20% (from 0.58 to 0.70). It also exhibits good robustness under different datasets and parameter configurations. In addition, after one semester of application, the pass rate and excellent rate of students have also shown significant improvement, verifying the practical value of the system in effectively improving learning outcomes in real educational scenarios.
Xu et al. (Mon,) studied this question.