Dual-LightGCN: Dual light graph convolutional network for discriminative recommendation

In recent years, graph neural networks have played a very important role in graph data analysis, and the application of graph convolutional networks (GCN) to recommender systems has been extensively investigated by recent studies. GCNs also recently demonstrated their potential to be analyzed from the point of view of Explainable Artificial Intelligence because of their underlying structure. However, most of the existing GCN-based methods are aggregated of information in one scale space and did not consider aggregation of information in multi-scale space. On this basis, this paper proposes an innovative dual light graph convolutional network model called Dual-LightGCN, which explicitly filters out items disliked by users to ensure more discriminative recommendation. Particularly, our model divides the original user-item interaction graph into two bipartite subgraphs, one subgraph is used to model the preferences between users and items, while the other is used to model the dislike relationships between them. For these two subgraphs, the LightGCN model recommendation is performed on them respectively. In the Movielens-1M dataset, the F1-score in Dual-LightGCN has increased by an average of 26%. We conducted a comprehensive evaluation of the proposed method on two datasets of different sizes and compared it with several state-of-the-art recommendation algorithms, and the results showed that the accuracy and F1-score results were significantly higher than those of other recommendation algorithms. The significantly low computational time required makes the proposed method suitable for successful deployment in various IoT scenarios.