对于便携式传感器设备的冷启动建议,内容意识的几次拍摄的元学习
Xiaomin Lv1, Kai Fang2, Tongcun Liu2
1School of Information Technology, The Zhejiang Shuren University, Hangzhou 310015, China.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
我们推出了一种新的内容意识的少量射击元学习 (CFSM) 模型,以解决便携式设备的序列建议中的冷启动问题. 通过有效学习用户和项目表示方式,CFSM显著提高了推准确性.
科学领域:
- 机器学习 机器学习
- 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:
- 冷启动问题是序列推系统的一个重大挑战,特别是在便携式传感器件中.
- 当前的内容意识方法缺乏通用性,并且难以有效地优先考虑内容特征.
- 新的数据处理往往会导致现有模型的性能下降.
研究的目的:
- 提出一种新的内容意识的少量射击元学习 (CFSM) 模型,以提高冷启动序列推的准确性.
- 解决现有方法在区分特征重要性和处理新数据方面的局限性.
- 在数据稀缺的情况下,提高推系统的稳定性和通用性.
主要方法:
- 开发了一种内容意识的短暂超级学习 (CFSM) 模型.
- 整合了一个双塔网络 (DT-Net),用于学习用户和项目表示.
- 使用元编码器和相互注意编码器来减轻噪音辅助信息.
- 采用了模型不可知的元优化策略,用于跨多种任务的培训.
主要成果:
- 在冷启动推场景中,CFSM在三个现实数据集 (ShortVideos,MovieLens,Book-Crossing) 中表现出卓越的性能.
- 该模型比第二最佳方法MetaCs-DNN.N.实现了1.55%,1.34%和2.42%的AUC改善.
- 拟议的DT-Net有效地减轻了噪音数据对辅助信息的影响.
结论:
- CFSM模型在解决序列建议的冷启动问题方面取得了重大进展.
- 超学习方法提高了模型的适应性和性能,使用有限的数据.
- CFSM提供了一个强大而准确的解决方案,用于在数据受限制的环境中提供个性化建议.
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