Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations


  • Rima Boughareb Department of Computer Science, Badji Mokhtar – Annaba University, Annaba, Algeria, LabGED Laboratory, Po box 12, Annaba, Algeria
  • Hassina Seridi-Bouchelaghem Department of Computer Science, Badji Mokhtar – Annaba University, Annaba, Algeria, LabGED Laboratory, Po box 12, Annaba, Algeria
  • Samia Beldjoudi National Higher School of Technology and Engineering, LTSE (Laboratoire de Technologies des Systèmes Energétiques), Annaba, Algeria



Recommender systems, Knowledge graphs, graph attention networks, graph embedding, Machine Learning, graph representation learning


The latest advances in Graph Neural Networks (GNNs), have provided important new ideas for solving the Knowledge Graph (KG) representation problem for recommendation purposes. Although GNNs have an effective graph representation capability, the nonlinear transformations over the layers cause a loss of semantic information and make the generated embeddings hard to explain. In this paper, we investigate the potential of large KGs to perform interpretable recommendation using Graph Attention Networks (GATs). Our goal is to fully exploit the semantic information and preserve inherent knowledge ported in relations by jointly learning low-dimensional embeddings for nodes (i.e., entities) and edges (i.e., properties). Specifically, we feed the original data with additional knowledge from the Linked Open Data (LOD) cloud, and apply GATs to generate a vector representation for each node on the graph. Experiments conducted on three real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. In addition to improving predictive performance in terms of precision, recall, and diversity, our approach fully exploits the rich structured information provided by KGs to offer explanation for recommendations.


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Author Biographies

Rima Boughareb, Department of Computer Science, Badji Mokhtar – Annaba University, Annaba, Algeria, LabGED Laboratory, Po box 12, Annaba, Algeria

Rima Boughareb is a PhD student at Badji Mokhtar – Annaba University, actively contributing to the research efforts of the esteemed Laboratory of Electronic Document Management (LabGED) Badji Mokhtar – Annaba University in Annaba, Algeria. She holds a Master’s degree in computer science from Annaba University (Algeria). She focuses on the semantic web, recommender systems, personalization, machine learning, and deep learning.

Hassina Seridi-Bouchelaghem, Department of Computer Science, Badji Mokhtar – Annaba University, Annaba, Algeria, LabGED Laboratory, Po box 12, Annaba, Algeria

Hassina Seridi-Bouchelaghem is a full professor at the Computer Science department of Badji Mokhtar – Annaba University, Algeria and is affiliated to LABGED Laboratory. She has published several papers in international conferences and journals. Her research interests include information systems, recommender systems, e-learning, semantic web, social web, data mining and artificial intelligence.

Samia Beldjoudi, National Higher School of Technology and Engineering, LTSE (Laboratoire de Technologies des Systèmes Energétiques), Annaba, Algeria

Samia Beldjoudi is currently an Associate Professor at the National Higher School of Technology and Engineering, and a Researcher at LTSE Laboratory. She received her Ph.D. degree in computer science from Annaba University (Algeria) and is affiliated to LABGED Laboratory. She has published several papers in international conferences and journals. Her main research interests include social semantic web, personalization, recommender systems, e-learning, deep learning, prognostics, CMMS, predictive maintenance, and artificial intelligence. She is also collaborating on several national projects.


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How to Cite

Boughareb, R. ., Seridi-Bouchelaghem, H. ., & Beldjoudi, S. . (2023). Joint Representation of Entities and Relations via Graph Attention Networks for Explainable Recommendations. Journal of Web Engineering, 22(04), 615–638.