This paper presents a semantic course recommendation system for students using a self-supervised contrastive learning approach built upon BERT (Bidirectional Encoder Representations from Transformers). Traditional BERT embeddings suffer from anisotropic representation spaces, where course descriptions exhibit high cosine similarities regardless of semantic relevance. To address this limitation, we propose a contrastive learning framework with data augmentation and isotropy regularization that produces more discriminative embeddings. Our system processes student text queries and recommends Top-N relevant courses from a curated dataset of over 500 engineering courses across multiple faculties. Experimental results demonstrate that our fine-tuned model achieves improved embedding separation and more accurate course recommendations compared to vanilla BERT baselines.

翻译：本文提出了一种面向学生的语义课程推荐系统，该系统采用基于BERT（来自Transformer的双向编码器表示）的自监督对比学习方法。传统的BERT嵌入存在表示空间各向异性的问题，导致课程描述无论语义相关性如何都表现出较高的余弦相似度。为克服这一局限性，我们提出了一种结合数据增强与各向同性正则化的对比学习框架，以生成更具区分度的嵌入表示。我们的系统处理学生文本查询，并从跨多个院系精选的500余门工程课程数据集中推荐Top-N相关课程。实验结果表明，与原始BERT基线模型相比，经过微调的模型实现了更好的嵌入分离效果和更精确的课程推荐。