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Deep Relational Factorization Machine Techniques for Content Usage Prediction via Multiple Interaction Types
Deep Relational Factorization Machine Techniques for Content Usage Prediction via Multiple Interaction Types
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机译:深度关系分解机技术通过多种交互类型进行内容使用预测
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摘要
A deep relational factorization machine (“DRFM”) system is configured to provide a high-order prediction based on high-order feature interaction data for a dataset of sample nodes. The DRFM system can be configured with improved factorization machine (“FM”) techniques for determining high-order feature interaction data describing interactions among three or more features. The DRFM system can be configured with improved graph convolutional neural network (“GCN”) techniques for determining sample interaction data describing sample interactions among sample nodes, including sample interaction data that is based on the high-order feature interaction data. The DRFM system generates a high-order prediction based on the high-order feature interaction embedding vector and the sample interaction embedding vector. The high-order prediction can be provided to a prediction computing system configured to perform operations based on the high-order prediction.
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