The application of recommendation technologies has been crucial in the promotion of physical and digital content across numerous global platforms such as Amazon, Apple, and Netflix. Our study aims to investigate the advantages of employing recommendation technologies on educational platforms, with a particular focus on an educational platform for learning and practicing music.

Our research is based on data from Tomplay, a music platform that offers sheet music with professional audio recordings, enabling users to discover and practice music content at varying levels of difficulty. Through our analysis, we emphasize the distinct interaction patterns on educational platforms like Tomplay, which we compare with other commonly used recommendation datasets. We find that interactions are comparatively sparse on educational platforms, with users often focusing on specific content as they learn, rather than interacting with a broader range of material. Therefore, our primary goal is to address the issue of data sparsity. We achieve this through entity resolution principles and propose a neural network (NN)-based recommendation model. Further, we improve this model by utilizing graph neural networks (GNNs), which provide superior predictive accuracy compared to NNs. Notably, our study demonstrates that GNNs are highly effective even for users with little or no historical preferences (cold-start problem).

Our cold-start experiments also provide valuable insights into an independent issue, namely, the number of historical interactions needed by a recommendation model to gain a comprehensive understanding of a user. Our findings demonstrate that a platform acquires a solid knowledge of a user’s general preferences and characteristics with 50 past interactions. Overall, our study makes significant contributions to information systems research on business analytics and prescriptive analytics. Moreover, our framework and evaluation results offer implications for various stakeholders, including online educational institutions, education policymakers, and learning platform users.

推荐技术的应用对于在亚马逊、苹果和 Netflix 等众多全球平台上推广实体和数字内容至关重要。我们的研究旨在调查在教育平台上采用推荐技术的优势，特别关注学习和练习音乐的教育平台。

我们的研究基于 Tomplay 的数据，Tomplay 是一个音乐平台，提供带有专业录音的乐谱，使用户能够发现和练习不同难度的音乐内容。通过我们的分析，我们强调了 Tomplay 等教育平台上独特的交互模式，并将其与其他常用的推荐数据集进行了比较。我们发现教育平台上的互动相对较少，用户在学习时通常专注于特定内容，而不是与更广泛的材料进行互动。因此，我们的首要目标是解决数据稀疏问题。我们通过实体解析原则实现这一点，并提出基于神经网络（NN）的推荐模型。此外，我们通过利用图神经网络（GNN）改进了这个模型，与神经网络相比，它提供了更高的预测精度。值得注意的是，我们的研究表明，即使对于历史偏好很少或没有的用户（冷启动问题），GNN 也非常有效。

我们的冷启动实验还为一个独立问题提供了有价值的见解，即推荐模型需要多少历史交互次数才能全面了解用户。我们的研究结果表明，平台通过过去 50 次交互，对用户的一般偏好和特征有了扎实的了解。总的来说，我们的研究对业务分析和规范分析的信息系统研究做出了重大贡献。此外，我们的框架和评估结果为各种利益相关者提供了影响，包括在线教育机构、教育政策制定者和学习平台用户。