深度神经协作过模型用于个性化的旅行建议
K Aarif1, J Deepika2, M Ashwin Kumar1
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Scientific reports
|January 13, 2026
概括
本研究引入了一个神经协作过 (NCF) 模型,以增强个性化的旅行建议. 通过克服传统方法的局限性,NCF模型显著提高了准确性和用户满意度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 推系统是一个推系统.
背景情况:
- 个性化旅行建议对于用户体验至关重要.
- 传统的协作过方法面临着数据稀疏性和冷启动问题等挑战.
- 这些局限性导致不理想的旅行预测和用户满意度.
研究的目的:
- 开发一个先进的个性化旅行推系统.
- 通过一种新的方法来解决旅行计划中的各种用户偏好.
- 克服传统协作过模型的局限性.
主要方法:
- 实现一个神经协作过 (NCF) 模型.
- 使用神经网络来学习复杂的用户旅行关系.
- 采用多层感知器,根据用户交互进行精细预测.
主要成果:
- 该NCF模型显著优于传统的推方法.
- 在预测准确性和用户满意度方面取得了明显的改进.
- 有效处理数据稀疏性和不同的用户偏好.
结论:
- 拟议的NCF模型推进了个性化的旅行建议.
- 神经网络为复杂的用户旅行动态提供了强大的解决方案.
- 该系统通过更准确和多样化的旅行建议来增强用户体验.
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