ImprovedGCN: An efficient and accurate recommendation system employing lightweight graph convolutional networks in social media

Graph Convolutional Networks (GCNs) have emerged as a hot topic of interest for collaborative filtering among researchers in the recent past. The research which exists in literature and is applied to recommendation does not analyze all the facets of GCN, as GCN is introduced for graph classification activities. It is observed that the two facets of GCNs namely, feature transformation and non-linear activation have a small influence on increasing the effectiveness of collaborative filtering (CF). Furthermore, the inclusion of these two facets increases the complexity of training and even decreases the recommendation performance. In this paper, a novel approach namely Improved Graph Convolutional Network (ImprovedGCN) has been proposed which only makes use of the important part of GCN termed neighborhood aggregation for CF. The aforesaid model can be implemented and trained which leads to significant improvements as compared to a similar approach termed Neural Graph Collaborative Filtering (NGCF).

Automated summary

This paper introduces an improved Graph Convolutional Network (ImprovedGCN) for collaborative filtering, which utilizes the important part of GCN called neighborhood aggregation. Compared to existing approaches, this model significantly improves the recommendation performance.