.png){kind=icon}
返回
Extreme Residual Connected Convolution-Based Collaborative Filtering for Document Context-Aware Rating Prediction
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2020.2981088.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning methods can improve the performance of recommender systems over traditional methods, especially when text information is available. To extract information hidden in the text description of an item and fuse it with rating information, it has been proposed that the network be stacked with more layers. However, as the network thus deepens, the problem of the attenuation of the preamble signal can cause the convolutional neural network to break down. The gradient gradually disappears during the back-propagation process, resulting in an inability to adjust the weights. In this paper, a method called eXtreme Residual connected Convolution Collaborative Filtering (xRConvCF) is proposed to predict the rating for each item based on the textual information. It creates data branch lines to form a residual module called the eXtreme residual (xRes) connection to mitigate the problem of the vanishing gradient and enhance feature reuse. The results of experiments on empirically obtained datasets show that the proposed deep learning model significantly outperforms state-of-the-art methods of recommendation.
Keyword:
Residual connection
collaborative filtering
recommender system
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:
9.8W
被引数:
29.4W
机构
引用论文
.png){kind=icon}
Analytical Representation of Measured Lightning Currents and Its Application to Electromagnetic Field Estimation测量雷电电流的分析表示及其在电磁场估计中的应用
PREAI
分享收藏
查看更多内容