Using Neural and Graph Neural Recommender systems to Overcome Choice Overload: Evidence from a Music Education Platform

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hédi Razgallah, Michalis Vlachos, Ahmad Ajalloeian, Ninghao Liu, Johannes Schneider, Alexis Steinmann
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引用次数: 0

Abstract

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.

使用神经和图神经推荐系统克服选择过载:来自音乐教育平台的证据
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时,推荐技术的应用至关重要。我们的研究旨在探讨在教育平台上应用推荐技术的优势,尤其关注音乐学习和练习的教育平台。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,使用户能够发现并练习不同难度的音乐内容。通过分析,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,教育平台上的交互相对稀少,用户在学习过程中往往只关注特定内容,而不是与更广泛的材料进行交互。因此,我们的首要目标是解决数据稀少的问题。我们通过实体解析原则来实现这一目标,并提出了一个基于神经网络 (NN) 的推荐模型。此外,我们还利用图神经网络(GNN)改进了这一模型,与神经网络相比,图神经网络具有更高的预测准确性。值得注意的是,我们的研究表明,即使用户很少或没有历史偏好(冷启动问题),图神经网络也非常有效。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,即推荐模型全面了解用户所需的历史交互数量。我们的研究结果表明,一个平台通过过去 50 次互动就能获得关于用户一般偏好和特征的可靠知识。总之,我们的研究为商业分析和描述性分析方面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对在线教育机构、教育政策制定者和学习平台用户等各利益相关方都有借鉴意义。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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