Improving Graph Convolutional Networks with Transformer Layer in social-based items recommendation
This work addresses social-based item recommendation, but it appears incremental as it builds on existing GCN methods with transformer modifications.
The paper tackled the problem of predicting ratings in social networks by enhancing Graph Convolutional Networks (GCN) with transformer layers, resulting in improved performance over standard GCN on link prediction tasks.
In this work, we have proposed an approach for improving the GCN for predicting ratings in social networks. Our model is expanded from the standard model with several layers of transformer architecture. The main focus of the paper is on the encoder architecture for node embedding in the network. Using the embedding layer from the graph-based convolution layer, the attention mechanism could rearrange the feature space to get a more efficient embedding for the downstream task. The experiments showed that our proposed architecture achieves better performance than GCN on the traditional link prediction task.