Abstract
MOOC recommendation systems have received increasing attention to help learners navigate and select preferred learning content. Traditional methods such as collaborative filtering and content-based filtering suffer from data sparsity and over-specialization. To alleviate these limitations, graph-based approaches have been proposed; however, they still rely heavily on manually predefined metapaths, which often capture only superficial structural relationships and impose substantial burdens on domain experts as well as significant engineering costs. To overcome these limitations, we propose AMR (Aspect-aware MOOC Recommendation), a novel framework that models path-specific multiple aspects by embedding the semantic content of nodes within each metapath. AMR automatically discovers metapaths through bi-directional walks, derives aspect-aware path representations using a bi-LSTM-based encoder, and incorporates these representations as edge features in the learner-learner and KC-KC subgraphs to achieve fine-grained semantically informed KC recommendations. Extensive experiments on the large-scale MOOCCube and PEEK datasets show that AMR consistently outperforms state-of-the-art graph neural network baselines across key metrics such as HR@K and nDCG@K. Further analysis confirms that AMR effectively captures rich path-specific aspect information, allowing more accurate recommendations than those methods that rely solely on predefined metapaths. The code will be available upon accepted.
Abstract (translated)
MOOC推荐系统越来越受到关注，旨在帮助学习者导航并选择他们偏好的学习内容。传统的方法如协同过滤和基于内容的过滤会因为数据稀疏性和过度专业化而存在局限性。为了缓解这些问题，图基方法已经被提出；然而，这些方法仍然严重依赖于手动预定义的元路径（metapaths），这往往只能捕捉到表层结构关系，并且给领域专家带来了沉重负担以及显著的技术成本。 为了解决这些限制，我们提出了AMR（Aspect-aware MOOC Recommendation）这一新颖框架。该框架通过在每个元路径中嵌入节点的语义内容来建模特定于路径的多个方面。AMR能够自动发现元路径，通过双向行走的方式，并使用基于双层长短期记忆网络（bi-LSTM-based encoder）的方法生成考虑了各方面信息的路径表示。此外，这些表示被用作学习者-学习者以及知识点-知识点子图中的边特征，从而实现细粒度且语义丰富的知识点推荐。 在大规模MOOCCube和PEEK数据集上的广泛实验显示，AMR在诸如HR@K和nDCG@K等关键指标上始终优于最先进的基于图神经网络的方法。进一步分析证实了AMR能够有效地捕捉丰富、特定于路径的方面信息，从而比那些仅仅依赖于预定义元路径的方法做出更准确的推荐。 研究团队将在论文被接受后开放代码供下载使用。
URL
https://arxiv.org/abs/2602.05297
