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"abstract": "Graph Convolution Networks (GCNs) are playing important role and widely used in recommendation systems. This is benefited from their capability of capturing the collaborative signals of higher-order neighbors by exploiting the graph structure. GCN-based methods have made great success in improving recommending performance, but still suffer from the severe problem of data sparsity. An effective solution to alleviate the data sparsity is to introduce attribute information. However, existing GCN-based methods hardly capture the complex attribute information of users and items and the complicated relationships between users, items, and attributes simultaneously. To address the above problems, we propose a novel attribute-fusing graph convolution network model called AF-GCN. Specifically, we first propose an attention-based attribute fusion strategy by taking account of different effects of attributes. Then, we construct a complex graph containing four kinds of nodes. Finally, we design a particular Laplacian matrix, which leverages the attribute information through graph structure to learn user and item representations better. Extensive experimental results on three real-world datasets demonstrate that the proposed AF-GCN significantly outperforms state-of-the-art methods. The source codes of this work are available at <uri>https://github.com/xiaorui-mnaire/af-gcn</uri>.",
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"abstract": "Ischemic stroke is a leading cause of disability and death worldwide among adults. The individual prognosis after stroke is extremely dependent on treatment decisions physicians take during the acute phase. In the last five years, several scores such as the ASTRAL, DRAGON, and THRIVE have been proposed as tools to help physicians predict the patient functional outcome after a stroke. These scores are rule-based classifiers that use features available when the patient is admitted to the emergency room. In this paper, we apply machine learning techniques to the problem of predicting the functional outcome of ischemic stroke patients, three months after admission. We show that a pure machine learning approach achieves only a marginally superior Area Under the ROC Curve (AUC) (Z_$0.808\\pm 0.085$_Z) than that of the best score (Z_$0.771\\pm 0.056$_Z) when using the features available at admission. However, we observed that by progressively adding features available at further points in time, we can significantly increase the AUC to a value above 0.90. We conclude that the results obtained validate the use of the scores at the time of admission, but also point to the importance of using more features, which require more advanced methods, when possible.",
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