Abstract
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.
Abstract (translated)
这项研究提出了LIT-GRAPH（用于推荐和教学启发的文献图），这是一种基于知识图谱的推荐系统，旨在帮助高中英语教师选择多样且符合教育目标的教学文学作品。该系统建立在英语文学本体论的基础上，解决了课程停滞的问题，并比较了四种图嵌入范式：DeepWalk、偏置随机游走（BRW）、混合型（结合DeepWalk和BRW向量）以及深度模型关系图卷积网络（R-GCN）。研究结果揭示了一个关键的差异：浅层模型在结构链接预测方面表现出色，而R-GCN则在语义排序上占据主导地位。通过利用特定于关系的消息传递，深度模型优先考虑教育相关性而非原始连接度，在领域特定推荐的质量和实用性方面取得了卓越成果。
URL
https://arxiv.org/abs/2602.07307
