Explainable Recommendation Based on Weighted Knowledge Graphs and Graph Convolutional Networks

Rima Boughareb, Hassina Seridi and Samia Beldjoudi
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Rima Boughareb: Department of Computer Science, Badji Mokhtar - Annaba University, Annaba, Algeria†LabGED Laboratory, Badji Mokhtar - Annaba University, P.O. Box 12, Annaba, Algeria
Hassina Seridi: Department of Computer Science, Badji Mokhtar - Annaba University, Annaba, Algeria†LabGED Laboratory, Badji Mokhtar - Annaba University, P.O. Box 12, Annaba, Algeria
Samia Beldjoudi: ��LabGED Laboratory, Badji Mokhtar - Annaba University, P.O. Box 12, Annaba, Algeria‡The Higher School of Industrial Technologies, Badji Mokhtar - Annaba University, Annaba, Algeria

Journal of Information & Knowledge Management (JIKM), 2023, vol. 22, issue 03, 1-25

Abstract: Knowledge Graphs (KGs) have been shown to have great potential to provide rich and highly defined structured data about Recommender Systems (RSs) items. This paper introduces Explain- KGCN, an Explainable RS based on KGs and Graph Convolutional Networks (GCNs). The system emphasises the importance of semantic information characterisation and high-order connectivity of message passing to explore potential user preferences. Thus, based on a relation-specific neighbourhood aggregation function, it aims to generate for each given item a set of relation-specific embeddings that depend on each semantic relation in the KG. Specifically, the relation-specific aggregator discriminates neighbours based on their relationship with the target node, allowing the system to model the semantics of various relationships explicitly. Experiments conducted on two real-world datasets for the top-K recommendation task demonstrate the state-of-the-art performance of the system proposed. Besides improving predictive performance in terms of precision and recall, Explain-KGCN fully exploits wealthy structured information provided by KGs to offer recommendation explanation.

Keywords: Recommender systems; knowledge graphs; graph convolutional networks; graph embedding; machine learning; graph representation learning (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0219649222500988

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