Neighbor Interaction Aware Graph Convolution Networks for Recommendation

Sun, Junhua; Zhang, Yingxue; Guo, Wei; Guo, Huifeng; Tang, Ruiming; He, Xiuqiang; Ma, Chen; Coates, Mark

doi:10.1145/3397271.3401123

Cited by 127 publications

(76 citation statements)

References 26 publications

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“…Recently, the NAN has received increasing attention from academia, and it is often applied in recommendation systems. [33][34][35] Because, in recommendation systems, the input data are graphs, where there are data nodes and connections naturally. However, in the medical image classification, the images are isolated because there are no built-in connections between the images, and the patients are usually strangers in real-world situations.…”

Section: Neighboring Aware Network (Nans)mentioning

confidence: 99%

NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network

Zhu

Górriz

et al. 2021

Int J of Intelligent Sys

126

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COVID‐19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer‐aided diagnosis system based on artificial intelligence to automatically identify the COVID‐19 in chest computed tomography images. We utilized transfer learning to obtain the image‐level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the k ‐nearest neighbors algorithm, in which the ILRs were linked with their k ‐nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end‐to‐end COVID‐19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state‐of‐the‐art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID‐19, which can be used in clinical diagnosis.

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Section: Neighboring Aware Network (Nans)mentioning

confidence: 99%

NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network

Zhu

Górriz

et al. 2021

Int J of Intelligent Sys

126

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“…There are many irregular data in the form of graphs in real life, such as social networks, knowledge graphs, and so on. The graph convolutional network (GCN) has become a key research direction because of its great advantages in extracting the characteristics of irregular graph data [29], [30], [55], [70], [140], [141].…”

Section: B Model-based Cf Algorithmsmentioning

confidence: 99%

An Overview of Recommendation Techniques and Their Applications in Healthcare

Yue

Wang

Zhang

et al. 2021

IEEE/CAA J. Autom. Sinica

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With the increasing amount of information on the internet, recommendation system (RS) has been utilized in a variety of fields as an efficient tool to overcome information overload. In recent years, the application of RS for health has become a growing research topic due to its tremendous advantages in providing appropriate recommendations and helping people make the right decisions relating to their health. This paper aims at presenting a comprehensive review of typical recommendation techniques and their applications in the field of healthcare. More concretely, an overview is provided on three famous recommendation techniques, namely, content-based, CF-based, and hybrid methods. Next, we provide a snapshot of five application scenarios about health RS, which are dietary recommendation, lifestyle recommendation, training recommendation, decision-making for patients and physicians, and disease-related prediction. Finally, some key challenges are given with clear justifications to this new and booming field.

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“…• DGCF [10]: DGCF disentangled the representations of users and items at the granularity of user intents since a user generally had multiple intents to adopt certain items. • NIA-GCN [2]: NIA-GCN proposed a cross-depth ensemble layer to preserve the relational information in neighborhood. • NGAT4rec [9]: NGAT4rec generated the embeddings of neighbors according to the corresponding attention coefficients.…”

Section: G Model Trainingmentioning

confidence: 99%

“…To alleviate information overload on the web, recommender systems have been widely applied to many online services such as E-commerce and advertising [1], [2]. The goal of recommender systems is to predict whether a user will interact with an item, e.g., click, rate, purchase.…”

Section: Introductionmentioning

confidence: 99%

“…As an effective solution, CF achieves the prediction via exploiting past user-item interactions. In CF, substantial research focuses on learning the embeddings of users and items to predict a user's preference for an item based on the similarity of the embeddings [2], [3]. As many user-item interaction data show graph structures, GCNs [4], [5] have been widely applied to CF [6], [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Recurrent Neural Graph Collaborative Filtering

Zhang¹

2021

Proceedings of the 33rd International Conference on Software Engineering and Knowledge Engineering

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Collaborative Filtering (CF) is a prevalent technique in recommender systems. Substantial research focuses on learning the embedding of users and items via exploiting past user-item interactions. Recent years have witnessed the boom of Graph Convolutional Networks (GCNs) on CF. Performing graph convolution iteratively, GCN-based models concatenate/average/sum all outputs from different graph convolution layers to generate the embeddings of users and items. Although the previous methods have been proven effective, the pooling operations in the previous methods fail to consider the outputs from different graph convolution layers have different weights and the weights are related to sequential dependencies from precursor nodes. To resolve the aforementioned problems, in this work, we present a new model, Recurrent Neural Graph Collaborative Filtering (RNGCF), which proposes a sequential dependency construction module to adaptively generate the embeddings. Specifically, the module applies a gated recurrent unit (GRU) to learn the sequential dependencies from precursor nodes and an adaptive gated unit (AGU) to adaptively construct the embeddings based on the sequential dependencies. Extensive experiments on three benchmark datasets show that our model outperforms state-ofthe-art models consistently. Our implementation is available in PyTorch 1 .

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Neighbor Interaction Aware Graph Convolution Networks for Recommendation

Cited by 127 publications

References 26 publications

NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network

NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network

An Overview of Recommendation Techniques and Their Applications in Healthcare

Recurrent Neural Graph Collaborative Filtering

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