  The success of neural network embeddings has entailed a renewed interest in
using knowledge graphs for a wide variety of machine learning and information
retrieval tasks. In particular, recent recommendation methods based on graph
embeddings have shown state-of-the-art performance. In general, these methods
encode latent rating patterns and content features. Differently from previous
work, in this paper, we propose to exploit embeddings extracted from graphs
that combine information from ratings and aspect-based opinions expressed in
textual reviews. We then adapt and evaluate state-of-the-art graph embedding
techniques over graphs generated from Amazon and Yelp reviews on six domains,
outperforming baseline recommenders. Additionally, our method has the advantage
of providing explanations that involve the coverage of aspect-based opinions
given by users about recommended items.
