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

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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.

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Author notes

  1. Jamal Riffi, Abdelouahed Sabri, Badraddine Aghoutane, Ali Yahyaouy and Hamid Tairi have contributed equally.

Authors and Affiliations

  1. LISAC Laboratory, Department of Informatics, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, 1796, Fez-Atlas, Fez, Morocco

    Driss El Alaoui, Jamal Riffi, Abdelouahed Sabri, Ali Yahyaouy & Hamid Tairi

  2. Team of Processing and Transformation of Information Polydisciplinary Faculty of Errachidia, Moulay Ismaïl University, 11201, Zitoune Meknes, Morocco

    Badraddine Aghoutane

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  1. Driss El Alaoui
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  2. Jamal Riffi
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Correspondence to Driss El Alaoui.

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El Alaoui, D., Riffi, J., Sabri, A. et al. Deep GraphSAGE-based recommendation system: jumping knowledge connections with ordinal aggregation network. Neural Comput & Applic 34, 11679–11690 (2022). https://doi.org/10.1007/s00521-022-07059-x

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