Intelligent Recommendation System for Personalized Learning Resources for College Students Based on Image Processing

Abstract

With the rise of personalized learning, college students' demands for learning resources have become increasingly diversified. Traditional recommendation systems can no longer fully meet their needs for personalization and precision. Especially today, with an abundance of image resources, how to enhance the effectiveness of learning resource recommendation systems from a visual perspective has become a new challenge in the field of educational technology. This study proposes an intelligent recommendation system for personalized learning resources for college students, based on image processing. The system first implements semantic annotation of images that integrates contextual information through the granular computing concept and a second-order Conditional Random Field (CRF) model, improving the precision of annotations and the accuracy of semantic recognition. Secondly, the study explores an image retrieval method based on product quantization sparse coding, combined with edge feature descriptors and an optimized codebook, effectively enhancing the accuracy of learning resource retrieval and the relevance of recommendations. This research not only expands the application of image processing in the field of intelligent recommendation but also provides college students with more precise and personalized learning resource recommendation services.