Design and implementation of a video resource recommendation system leveraging social tagging

With the rapid growth of video resources on online education platforms, accurately recommending resources that meet learners’ needs has become crucial. This paper proposes a personalized resource recommendation model based on social tags, aiming to address the challenges of cold start and data sparsity, thereby enhancing the prediction accuracy of recommendation platforms. The model generates video tags by analyzing descriptive information, user annotations, and comments associated with videos, and constructs dynamic learner profiles. The similarity between video tags and learner profiles is calculated using Euclidean distance to recommend resources that meet learners’ personalized needs. Experimental results demonstrate that, compared to traditional user-based and item-based collaborative filtering algorithms, our model achieves approximately 15% and 8% improvements in precision and recall. These findings not only enhance learning efficiency but also significantly improve learner engagement and loyalty, showcasing the model’s significant application potential in the educational domain.