使用人工智能进行消费者行为分析,以获得值得信赖的曲目建议,而不是音乐舞蹈电子产品
1Xinxiang University, Xinxiang, 453003, China.
Heliyon
|September 26, 2024
概括
本研究介绍了一种基于人工智能的方法,用于可靠的音乐曲目建议. 它通过准确预测消费者对电子舞蹈音乐产品的偏好来增强用户体验.
科学领域:
- 人工智能的人工智能
- 推系统是一个推系统.
- 数字音乐 数字音乐 数字音乐
背景情况:
- 在数字时代,准确的音乐建议对于用户体验和消费者参与至关重要.
- 消费者行为建模对于提高推系统准确性至关重要.
- 现有的方法在个性化的音乐建议中往往缺乏可靠性.
研究的目的:
- 为音乐舞蹈电子产品提供可靠的跟踪建议提出一种新的AI授权方法.
- 改进数字音乐中的用户兴趣学习和消费者行为分析.
- 提高电子音乐推系统的准确性和可靠性.
主要方法:
- 集成的协作过和隐藏的马尔科夫模型来分析消费者行为模式.
- 开发了一种多层次的注意力表示学习方法,用于推.
- 利用散射转换用于音频预处理和基于注意力的独立循环神经网络,用于偏好编码.
主要成果:
- 拟议的AI授权方法显著提高了推准确性.
- 该系统在电子舞蹈音乐的曲目建议中表现出更高的可信度.
- 在真实世界数据集上的实验结果验证了该方法的优越性.
结论:
- 人工智能驱动的消费者行为分析为值得信赖的音乐建议提供了强大的方法.
- 集成先进的机器学习技术可以提高推器系统的性能.
- 这种方法为改善用户体验和与电子舞蹈音乐产品的互动提供了有价值的工具.
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