A Recommendation Method Based on Fusion of Graph Neural Network Single-Layer Mixing Negative Samples
Biao Cai,
Keyi Zhou,
Jing Cheng
et al.
Abstract:In deep neural network recommendation models, a common training method is to provide both positive and negative examples to the model to construct the loss function, in order that the model can better learn the useful information in the data because of the increased distinction between positive and negative examples. Due to their focus on strong negative examples, traditional negative sampling methods tend to select false negative samples, which leads to overfitting, reduces the generalization ability of the m… Show more
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