This study presents LIT-GRAPH (Literature Graph for Recommendation and Pedagogical Heuristics), a novel knowledge graph-based recommendation system designed to scaffold high school English teachers in selecting diverse, pedagogically aligned instructional literature. The system is built upon an ontology for English literature, addressing the challenge of curriculum stagnation, where we compare four graph embedding paradigms: DeepWalk, Biased Random Walk (BRW), Hybrid (concatenated DeepWalk and BRW vectors), and the deep model Relational Graph Convolutional Network (R-GCN). Results reveal a critical divergence: while shallow models excelled in structural link prediction, R-GCN dominated semantic ranking. By leveraging relation-specific message passing, the deep model prioritizes pedagogical relevance over raw connectivity, resulting in superior, high-quality, domain-specific recommendations.

翻译：本研究提出了LIT-GRAPH（用于推荐与教学启发式的文献图谱），这是一种基于知识图谱的新型推荐系统，旨在帮助高中英语教师选择多样化且与教学法相匹配的教学文献。该系统构建于一个英语文学本体之上，旨在应对课程内容停滞的挑战。我们比较了四种图嵌入范式：DeepWalk、偏置随机游走（BRW）、混合模型（拼接的DeepWalk与BRW向量）以及深度模型关系图卷积网络（R-GCN）。结果揭示了一个关键差异：浅层模型在结构链接预测方面表现出色，而R-GCN在语义排序方面占据主导地位。通过利用关系特定的消息传递机制，该深度模型将教学相关性置于原始连接性之上，从而实现了更优的、高质量的、领域特定的推荐。