基于知识图和多任务学习功能的音乐推算法
Xinqiao Liu1, Zhisheng Yang2, Jinyong Cheng3
1School of Music, Qufu Normal University, Rizhao, 276826, China.
Scientific reports
|January 24, 2024
概括
本研究介绍了MMSS_MKR框架,通过整合知识图表来增强音乐建议. 该模型有效地解决了稀疏性和冷启动问题,从而导致更准确的音乐建议.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信息检索 信息检索
背景情况:
- 音乐推系统经常遭受数据稀疏和冷启动问题.
- 辅助信息对于提高音乐推准确度至关重要.
研究的目的:
- 提出一个端到端的框架,MMSS_MKR,利用知识图表进行音乐推.
- 为了解决音乐建议中的稀疏性和冷启动挑战.
主要方法:
- MMSS_MKR框架使用知识图作为辅助信息的来源.
- 交叉和压缩单元桥梁知识图嵌入和推任务.
- 该模型通过知识图和推模块来提炼三重信息.
- 分别在推和嵌入模块中使用多个预测和计算.
- 改进的损失函数提高了建议的信息实用性.
主要成果:
- MMSS_MKR模型显示了与现有推模型相比的显著改进.
- 该框架有效地将知识图嵌入与推任务相结合.
- 这种方法成功地过出错误的三重信息,从而获得更现实的数据.
结论:
- MMSS_MKR为音乐推挑战提供了一个强大的解决方案.
- 知识图集成提高了音乐推系统的准确性和有效性.
- 拟议的框架为个性化推领域提供了宝贵的贡献.
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