Graph Neural Networks for Social Recommendation

WWW '19: The Web Conference on The World Wide Web Conference WWW 2019, pp. 417-426, 2019.

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In order to evaluate the quality of the recommendation algorithms, two popular metrics are adopted to evaluate the predictive accuracy, namely Mean Absolute Error and Root Mean Square Error

Abstract:

In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user...More

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