ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud

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Knowledge-aware Autoencoders for Explainable Recommender Systems (2018, methodology): “Other interesting works include: a RS based on Restricted Boltzmann Machines which looks at the rating distribution to identify the most explainable items [1], a Latent Factor Model leveraging users reviews to compute more transparent recommendations [23] and, finally, a novel approach based on movi”
Computing Recommendations via a Knowledge Graph-aware Autoencoder (2018, background): “Further applications of DBpedia properties embrace some interesting tasks like the generation of effective natural-language recommendation explanations [28] and the definition of semantic similarity measures for providing more accurate recommendations [23, 29, 35].”