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双模块-更宽,更深的随机梯度下降和脱落基于电影推的密集神经网络

Raghavendra C K¹, Srikantaiah K C², Sunil C K³

¹Department of Computer Science and Engineering, B.M.S. College of Engineering, Bull Temple Road, Bengaluru, 560019, KA, India. raghavendrack.cse@bmsce.ac.in.

Scientific reports

|December 5, 2025

概括

一个新的双模块更深入,更全面的密集神经网络 (DNN) 模型改善了电影推. 这种机器学习方法提高了使用用户评级的流媒体服务的项目建议准确性.

科学领域:

计算机科学计算机科学
机器学习机器学习
人工智能的人工智能

背景情况:

项目推对于Netflix和电子商务平台等流媒体服务至关重要.
推系统 (RS) 使用用户生成的数据,如评分,来预测用户偏好.
准确的电影推增强了用户体验和内容发现.

研究的目的:

为电影推开发和评估一个改进的密集神经网络 (DNN) 学习模型.
为了更全面的建议,包括分类和数值特征.
将拟议的DNN模型的性能与现有的推模型进行比较.

主要方法:

构建了一个更深入,更全面的双模块DNN模型.
该模型使用嵌入和密集层来处理各种特征.
实现了像静态梯度下降 (SGD) 和自适应时刻估计 (Adam) 等优化器,dropout 和修正线性单位 (ReLU) 激活.
该模型在Movie-Lens数据集 (100k和1M评分) 上进行了训练和评估.

主要成果:

拟议的DNN模型实现了0.16的平均平方误差 (MSE) 和0.33.3的平均绝对误差 (MAE).
该模型有效地结合了分类和数值特征.
修正线性单位 (ReLU) 激活功能有助于减轻过度拟合和改善收.

结论:

增强的DNN模型在电影推任务中表现出卓越的性能.
该模型提供了一个强大的方法来预测流媒体服务中的用户偏好.
这些发现表明,通过更准确的内容建议,可以提高用户参与度.

关键词:

密集的神经网络密集的神经网络放弃放弃放弃放弃这是一个双模块模块.电影推电影推

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