用对比学习优化BERT，让课程推荐更准
Isotropy-Optimized Contrastive Learning for Semantic Course Recommendation
- 引入数据增强和各向同性正则化，改善嵌入空间分布
- 在500+工程课程上实现更优的推荐准确率
- 适合需要精准语义匹配的教育推荐场景
本文提出一种基于BERT的自监督对比学习框架，用于学生课程语义推荐。传统BERT嵌入存在方向性偏差，导致不同课程描述间余弦相似度过高，难以区分语义差异。为此，我们设计了结合数据增强与各向同性正则化的对比学习方法，生成更具判别性的嵌入表示。系统处理学生文本查询，在包含500多门跨院系工程课程的精选数据集上推荐前N个相关课程。实验表明，相比原始BERT基线，该方法显著提升了嵌入分离度与推荐准确性。
原文摘要 · Abstract (English)
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.
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