Music College, Cangzhou Normal University, Cangzhou, Hebei 061000, China.
Art Department, Criminal Investigation Police University of China, Shenyang, Liaoning 110000, China.
Comput Intell Neurosci. 2022 Aug 9;2022:6529110. doi: 10.1155/2022/6529110. eCollection 2022.
In the context of Internet technology, the integration of information technology and education is a powerful supplement to the traditional teaching model of higher education. Online learning has become the new development direction of the education industry in the network era. To address the problems of serious difficulty in completing online teaching tasks, difficulty in monitoring teaching effects, and fragmentation of course resources in universities, a multimodal music knowledge graph is constructed. A personalized learning strategy based on users' interest is proposed through the mining of online education data, and a music online education system has been developed on this basis. To improve the recommendation accuracy of the model, an embedding propagation knowledge graph recommendation method based on decay factors is proposed. The model considers the changes in the strength of user interest during the intra- and interlayer propagation of the knowledge graph interest map and focuses on higher-order user potential interest representations for enhancing the semantic relevance of multihop entities. The experimental results show that the proposed model brings a good prediction effect on several benchmark evaluation metrics and outperforms other comparative algorithms regarding recommendation accuracy.
在互联网技术背景下，信息技术与教育的融合是对高校传统教学模式的有力补充。在线学习已成为网络时代教育行业的新发展方向。针对高校在线教学任务完成难、教学效果监控难、课程资源碎片化等问题，构建了一种多模态音乐知识图谱。通过对在线教育数据的挖掘，提出了一种基于用户兴趣的个性化学习策略，并在此基础上开发了一个音乐在线教育系统。为了提高模型的推荐精度，提出了一种基于衰减因子的嵌入传播知识图推荐方法。该模型考虑了知识图兴趣图在层内和层间传播过程中用户兴趣强度的变化，重点关注高阶用户潜在兴趣表示，以增强多跳实体的语义相关性。实验结果表明，所提出的模型在几个基准评估指标上都取得了良好的预测效果，在推荐精度方面优于其他对比算法。
