BS-CP:通过假设密度过的高效流动贝叶斯张量分解方法
Jiaqi Liu1, Qiwu Wu2, Lingzhi Jiang1
1School of Information Engineering, Engineering University of People's Armed Police of China, Xi'an, China.
PloS one
|December 2, 2024
概括
本研究介绍了BS-CP,这是一种高效的贝叶斯方法,用于更新张量分解模型与流数据. BS-CP在推系统等现实应用中显著提高了准确性和稳定性.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 数字分析 数字分析
背景情况:
- 张量数据在推系统等应用中很普遍,但通常会受到稀疏性,噪音和高生产率的影响.
- 贝叶斯张量分解为处理数据缺陷提供了优势,但面临着计算挑战,特别是流数据.
研究的目的:
- 开发一种高效和准确的方法,用于动态更新贝叶斯张量分解中潜在因子的后方,用于流数据.
- 用快速生成的数据流来解决贝叶斯式CP (CANDECOMP/PARAFAC) 分解的计算挑战.
主要方法:
- 拟议的BS-CP,一个用于动态后置更新的新结构.
- 引入了BS-CP1,一种使用假设密度过 (ADF) 的高效实现.
- 开发了BS-CP2,采用高斯 - 拉格尔正方形来改善噪声集成和经验结果.
主要成果:
- BS-CP1和BS-CP2在推系统数据集上显著改善了根平均平方误差 (RMSE) (例如,在MovieLens-1m和Fit Record上分别为31.8%和33.3%).
- 与最先进的方法相比,提出的方法显示了超过10%的改进,具有卓越的稳定性.
- 经验结果表明,BS-CP算法非常适合大数据集和现实世界的场景.
结论:
- BS-CP为流数据的贝叶斯张量分解提供了一个计算效率高,准确的方法.
- 拟议的算法有效地处理稀疏和杂的张量数据,在准确性和稳定性方面超过现有方法.
- 对于涉及动态张量分解的实时应用,BS-CP是一个有前途的解决方案.
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