一个基于自动编码器和潜在特征分析的混合推系统.
Shangzhi Guo1, Xiaofeng Liao1, Gang Li1
1College of Computer Science, Chongqing University, Chongqing 400044, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究介绍了AutoLFA,这是一个混合推系统,结合了Autoencoder和隐性特征分析 (LFA),以改善用户偏好预测. 通过整合不同的优势来提高推性能,AutoLFA的表现优于现有模型.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 推系统 (RS) 对于从大数据中提取见解至关重要.
- 识别用户隐藏的偏好是RS实施的一个关键挑战.
- 隐性特征分析 (LFA) 和深度神经网络 (DNN) 虽然很受欢迎,但也有其局限性.
研究的目的:
- 提出一种新的混合推模型,AutoLFA,可以克服个别LFA和基于DNN的方法的局限性.
- 通过整合自动编码器和LFA模型的优势来提高推性能.
主要方法:
- 开发了AutoLFA,这是一个混合模型,结合了Autoencoder和LFA.
- 使用Autoencoder和LFA在不同的度量空间中构建单独的推模型.
- 使用自适应权重策略集成这些模型.
主要成果:
- 在五个现实世界推数据集上评估了AutoLFA.
- 与七个最先进的模型相比,显示出明显优越的推性能.
- 在不同的场景中验证了混合方法的有效性.
结论:
- 拟议的AutoLFA模型有效地利用了Autoencoder和LFA的互补优势.
- 混合化与自适应加权提供了一个强大的解决方案,以改善推系统.
- 在个性化推技术方面,AutoLFA提供了显著的进步.
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