一个基于对推系统的动态评估的新邻居选择方案.
Kerui Hu1, Lemiao Qiu1, Shuyou Zhang1
1State Key Laboratory of Fluid Power Transmission & Control, Zhejiang University, Hangzhou, China.
Science progress
|June 9, 2023
概括
本研究引入了一种新的邻居选择方法,用于协作过,该方法考虑了用户偏好和数据稀疏性的变化. 它通过动态加权用户数据来提高推准确性.
科学领域:
- 计算机科学 计算机科学
- 信息检索 信息检索
- 机器学习 机器学习
背景情况:
- 协作过 (CF) 是一个关键的推技术.
- 现有的CF方法与动态用户偏好和数据稀疏性作斗争.
- 评估CF推的有效性仍然是一个挑战.
研究的目的:
- 提出一个新的邻居选择方案,用于协作过.
- 解决动态用户偏好建模和推评估中的局限性.
- 在稀疏的数据环境中提高推性能.
主要方法:
- 引入了偏好衰退期的概念,以建模用户偏好演变.
- 定义了动态衰变因子,以减轻历史数据的影响.
- 开发了三个动态评估模块,用于用户的可信度和推能力.
- 实施了一种混合选择策略,具有两个邻居选择层和可调节的值.
主要成果:
- 拟议的方案有效地选择了有能力和值得信赖的邻居.
- 在三个真实数据集上的实验结果证明了优越的推性能.
- 该方法显示了与最先进的技术相比的显著改进,特别是在数据稀疏方面.
结论:
- 新的邻居选择方案通过动态适应用户偏好来增强协作过.
- 该方法在现实应用中提供了更好的推准确性和有效性.
- 这种方法为数据稀疏性和偏好动态带来的挑战提供了强有力的解决方案.
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