返回
Deep latent factor model for collaborative filtering
DOI:10.1016/j.sigpro.2019.107366.png)
摘要
En 中文
Latent factor models have been used widely in collaborative filtering based recommender systems. In recent years, deep learning has been successful in solving a wide variety of machine learning problems. Motivated by the success of deep learning, we propose a deeper version of latent factor model. Experiments on benchmark datasets shows that our proposed technique significantly outperforms all state-of-the-art collaborative filtering techniques. (C) 2019 Elsevier B.V. All rights reserved.
Keyword:
Deep learning
Latent semantic analysis
Collaborative filtering
Recommender systems
AI总结
对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
Enhancing the Silanization Reaction of the Silica-Silane System by Different Amines in Model and Practical Silica-Filled Natural Rubber Compounds
Polymers
IF0
Local coordinate based graph-regularized NMF for image representation基于局部坐标的图正则化NMF图像表示
SIGNAL PROCESSING
IF3.6

