SAN: Attention‐based social aggregation neural networks for recommendation system

Jiang, Nan; Gao, Li; Duan, Fuxian; Ju, Wen; Wan, Tao; Chen, Honglong

doi:10.1002/int.22694

Cited by 19 publications

(14 citation statements)

References 33 publications

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“…Recently, researchers have incorporated attention mechanisms into recommendation systems to improve performance. Attention is a method that helps a model identify which parts of the input data are most important for making a decision [14]. The attention method does the task of distinguishing the importance of data by learning a pattern in the data and using that pattern to focus on certain parts of the data more heavily when making a decision [11].…”

Section: Attention-based Recommendation Systems Using Deep Neural Net...mentioning

confidence: 99%

DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems

Wang¹,

Li²,

Shi³

et al. 2023

Preprint

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Online social media platforms offer access to a vast amount of information, but sifting through the abundance of news can be overwhelming and tiring for readers. personalised recommendation algorithms can help users find information that interests them. However, most existing models rely solely on observations of user behaviour, such as viewing history, ignoring the connections between the news and a user's prior knowledge. This can result in a lack of diverse recommendations for individuals. In this paper, we propose a novel method to address the complex problem of news recommendation. Our approach is based on the idea of dual observation, which involves using a deep neural network with observation mechanisms to identify the main focus of a news article as well as the focus of the user on the article. This is achieved by taking into account the user's belief network, which reflects their personal interests and biases. By considering both

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Section: Attention-based Recommendation Systems Using Deep Neural Net...mentioning

confidence: 99%

DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems

Wang¹,

Li²,

Shi³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Also, the application of computers can help to solve problems better 3 . Automated detection in biomedicine can speed up disease detection 4 . So far, there are still many problems that are not solved by computers in medical image segmentation 5 …”

Section: Introductionmentioning

confidence: 99%

“…3 Automated detection in biomedicine can speed up disease detection. 4 So far, there are still many problems that are not solved by computers in medical image segmentation. 5 However, the current computer hardware facilities are excellent enough to be able to analyze images very accurately.…”

Section: Introductionmentioning

confidence: 99%

CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

Liu

et al. 2022

Int J of Intelligent Sys

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Currently, deep learning has become more and more mature in the field of medical image segmentation. Through using the computer, the deep learning models established can completely help doctors to perform medical image segmentation. Most of the current deep learning models are based on Unet. The U-shaped structure and the skip connection layer of Unet can effectively achieve precise image segmentation. However, for complicated images, the network structure of Unet is not sufficient enough. In response to this problem, some scholars have designed Unet++ by adding a denser skip connection layer to U-Net. Compared to U-Net, Unet++ is more effective in dealing with complex images, but it has drawbacks in many aspects, and there is still a large loss of eigenvalues in the skip connection and upsampling processes. To address these issues, this paper uses the channel and attention mechanism to improve the Unet++ model to obtain better image segmentation efficiency and accuracy. Meanwhile, based on Unet++, this paper designs a new model called CA-Unet++.The proposed model uses the channel module and the attention module to solve the eigenvalues loses in the long-distance skip connection process and the upsampling process, respectively. The experimental results and data analysis shows that our proposed CA-Unet++

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“…Therefore, manually annotated images are often not in line with the actual situation, which has a significant impact on the diagnosis. With the development of computer technology, 1,2 computers have been able to help researchers solve problems in various fields 3,4 . It is a wise decision to improve the accuracy of image segmentation by computers 5 .…”

Section: Introductionmentioning

confidence: 99%

RT‐Unet: An advanced network based on residual network and transformer for medical image segmentation

Liu

et al. 2022

Int J of Intelligent Sys

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For the past several years, semantic segmentation method based on deep learning, especially Unet, have achieved tremendous success in medical image processing. The U-shaped topology of Unet can well solve image segmentation tasks. However, due to the limitation of traditional convolution operations, Unet cannot realize global semantic information interaction. To address this problem, this paper proposes RT-Unet, which combines the advantages of Transformer and Residual network for accurate medical segmentation. In RT-Unet, the Residual block is taken as the image feature extraction layer to alleviate the problem of gradient degradation and obtain more effective features. Meanwhile, Skip-Transformer is proposed, which takes Multi-head Self-Attention as the main algorithm framework, instead of the original Skip-Connection layer in Unet to avoid the influence of shallow features on the network's performance. Besides, we add attention module at the decoder to reduce semantic differences. According to the experiments on MoNuSeg data set and ISBI_2018cell data set, RT-Unet achieves better segmentation performance than other deep learning-based algorithms. In addition, a series of further ablation experiments were conducted on Residual network and

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SAN: Attention‐based social aggregation neural networks for recommendation system

Abstract: The recommender system is of great significance to alleviate information overload. The rise of online social networks leads to a promising direction-social recommendation. By injecting the interaction influence

Cited by 19 publications

References 33 publications

DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems

DOR: A Novel Dual-Observation-Based Approach for News Recommendation Systems

CA‐Unet++: An improved structure for medical CT scanning based on the Unet++ Architecture

RT‐Unet: An advanced network based on residual network and transformer for medical image segmentation

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