mlsanityData to 7 Oct 2026

Review of Explainable Graph-Based Recommender Systems

arXiv 2408.00166 ↗First posted 31 Jul 2024cs.IR cs.AI cs.LGcited by 1

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Abstract (arXiv)

Explainability of recommender systems has become essential to ensure users' trust and satisfaction. Various types of explainable recommender systems have been proposed including explainable graph-based recommender systems. This review paper discusses state-of-the-art approaches of these systems and categorizes them based on three aspects: learning methods, explaining methods, and explanation types. It also explores the commonly used datasets, explainability evaluation methods, and future directions of this research area. Compared with the existing review papers, this paper focuses on explainability based on graphs and covers the topics required for developing novel explainable graph-based recommender systems.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 64 of its 131 references that are in the map. First posted on arXiv 31 Jul 2024.

Builds on

64 of 131 references.

References · 64

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38 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026The Utility of LLMs in Recommender Systems Explanation Evaluationcited by 0