数字媒体推系统的设计基于用户行为分析和情绪特征提取
Ting Ruan1, Qian Liu1, Yanxizi Chang1
1School of art and design, Hubei University, Wuhan, Hubei, China.
PloS one
|May 19, 2025
概括
本研究介绍了ATLSTM-PS,这是一种使用用户情绪传播内容的新框架. 它增强了功能提取,并整合了用户行为,提供精确的建议,改善了数字平台上的用户体验.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 数字平台产生了大量的用户情绪数据,这对于理解行为至关重要.
- 目前分析这些数据和定制内容的方法有限.
- 以用户为中心的情感洞察对于优化内容策略和用户满意度至关重要.
研究的目的:
- 引入ATLSTM-PS框架,用于基于用户情绪的内容传播策略.
- 为了提高情感分析中的特征提取精度.
- 提高内容推的准确性和数字平台上的用户体验.
主要方法:
- 使用了一种增强的ATT-LSTM方法,具有精确情绪内容提取的注意力机制.
- 在功能层集成用户的行为和情绪属性.
- 开发了ATLSTM-PS框架,以综合这些综合特征,提供准确的建议.
主要成果:
- ATLSTM-PS框架在内容传播方面显著提高了效率.
- 在ATLSTM-PS中不同注意层的协同作用提高了推准确度.
- 经验结果验证了框架在不同数据集上的有效性.
结论:
- ATLSTM-PS提供了一种用于数字媒体情绪分析的新技术工具.
- 该框架为多媒体平台提供了一种强有力的方法,以完善信息传播.
- 这种方法可以通过个性化的内容建议来增强用户体验.
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