从评级预测转移学习到Top-k推
Fan Ye1, Xiaobo Lu1, Hongwei Li1
1Academy of Computer Science and Technology, Anhui University, Hefei, China.
PloS one
|March 28, 2024
概括
本研究引入了推者系统的通用转移模型,通过利用评级预测模型的信息来增强Top-k推任务. 拟议的BC-PMLP模型有效地转移学习的特征,提高推的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 推系统在评级预测 (回归) 和Top-k推 (分类) 中表现出色.
- 参数优化在这两个任务之间有很大的区别.
- 转移学习提供了一种潜在的方法,通过重用已学到的信息来弥合这一差距.
研究的目的:
- 为推者系统提出一个通用转移模型.
- 从评级预测模型中提取和利用Top-k推任务的信息.
- 通过转移学习的功能来提高Top-k推的性能.
主要方法:
- 开发了一种包含贝叶斯转换器 (BC) 和基于预测的多层感知器 (PMLP) 的通用传输模型.
- BC从评级预测模型中转换特征向量.
- PMLP提取预测评级,构建评级矩阵,并应用多层感知器来提高性能.
主要成果:
- 在四个基准数据集上证明了BC-PMLP模型的有效性.
- 使用了从单数值分解加加 (SVD++) 模型中提取的信息.
- 在Top-k推任务中表现优于经典和最先进的基线方法.
结论:
- 拟议的BC-PMLP模型成功地将知识从评级预测转移到Top-k推.
- 该模型显示了推绩效的显著改善.
- 进一步的实验证实了BC的实用性和参数变化的影响.
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