电影推模型基于使用混合AdaBoost集成的概率矩阵分解
Zhengjin Zhang1,2,3, Qilin Wu1,3, Yong Zhang1
1Chaohu University, Hefei, China.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了一种混合的AdaBoost组合方法,以增强推系统. 新方法比传统的概率矩阵因子化模型提高了预测准确性和稳定性.
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:
- 推系统对于流媒体平台至关重要.
- 概率矩阵因子化 (PMF) 面临着一般化和准确性的挑战.
- 像FCM-PMF,包装-BP-PMF和AdaBoost-SVM-PMF这样的现有模型都有自己的局限性.
研究的目的:
- 提出一种混合的AdaBoost组合方法,以提高推系统的性能.
- 为了解决PMF模型的概括能力差以及预测准确度低的问题.
- 为了提高用户项目评分预测准确性和模型稳定性.
主要方法:
- 使用模糊集群与会员函数和集群中心来计算用户项目评分矩阵.
- 用神经网络训练评分矩阵,以提高预测准确度.
- 使用神经网络作为基础学习者实施AdaBoost组合方法,使用投票来预测最终分数.
主要成果:
- 拟议的混合AdaBoost组合方法在MovieLens和FilmTrust数据集上显示了改进的性能.
- 与包装-BP-PMF模型相比,拟议的方法显示了平均绝对误差 (1.24%和0.79%) 和根-平均-平方误差 (2.55%和1.87%) 的轻微增加.
- 基于神经网络的学习者的权重提高了模型的稳定性和得分预测的普遍性.
结论:
- 混合的AdaBoost组合方法为推系统提供了一个强大的方法.
- 模糊集群,神经网络和AdaBoost的集成有效地提高了预测准确性和稳定性.
- 该方法的普遍性通过其成功的应用和加权学习者方法得到证实.
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