Deep GraphSAGE-based recommendation system: jumping knowledge connections with ordinal aggregation network

Abstract

Recommendation systems have become based on graph neural networks (GNN) as many fields, and this is due to the advantages that represent this kind of neural networks compared to the classical ones; notably, the representation of concrete realities by taking the relationships between data into consideration and understanding them in a better way. In this paper, we have proposed an item-based recommender system using a deep GraphSAGE model, which learns item embeddings from the user–item matrix and uses them for recommending items that are similar to the ones that users have interacted with before. Furthermore, we have discussed the common problems that usually arise when using deep GNN-based architectures, and which can negatively affect the performance of our recommender system, in particular, the over-smoothing problem. To this end, we have integrated the Jumping Knowledge connections (JK) strategy in our system, using a new method called Ordinal Aggregation Network (OAN) as a layer aggregator to tackle this kind of problem. To evaluate the recommendations, we have used the required metrics that are designated for this purpose: Hits@n and NDCG@n, and we have also measured the duration of training of every model. The experimental results that we have made show that our method has improved the performance of a recommender system concretely and efficiently compared to other aggregation methods. In addition, they have suggested that deep GraphSAGE with Jumping Knowledge connections (JK) would be empirically promising.