ENSG: Enhancing Negative Sampling in Graph Convolutional Networks for Recommendation Systems

In the field of recommendation, negative samples that are close to positive samples are referred to as “hard negative samples”. These hard negative samples are more likely to be incorrectly recommended to users. Therefore, researching how to enhance the ability to identify hard negative samples and thereby improve recommendation accuracy is an important direction in the field of recommendation systems. To address this issue, we propose a new model named Enhancing Negative Sampling in Graph Convolutional Networks for Recommendation Systems (ENSG). Firstly, part of the information in the positive sample was injected into the negative sample, and the high-quality, difficult negative sample was selected by the inner product method. Secondly, a lightweight graph convolutional network is used for feature extraction to obtain node representations for both users and items. Then, contrastive learning is used to maximize the similarity between the hard negative samples and the positive samples, so that the hard negative samples are as close to the positive samples as possible in the feature space. Ultimately, the model is trained by taking hard negative samples as input and minimizing the loss function to optimize model parameters; the recommendation accuracy of the model is enhanced. To validate the effectiveness of our approach, we conducted comparative evaluations utilizing three publicly accessible datasets. The experimental outcomes indicate that our proposed model surpasses the baseline model in terms of performance. On three public datasets, the algorithm proposed in this paper achieved the highest improvements of 16.27% and 12.72% in the evaluation metrics Recall@20 and NDCG@20, respectively.