  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, current recommendation methods based on graph
embeddings have shown state-of-the-art performance. These methods commonly
encode latent rating patterns and content features. Different 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. Our approach has the advantage of
providing explanations which leverage aspect-based opinions given by users
about recommended items. Furthermore, we also provide examples of the
applicability of recommendations utilizing aspect opinions as explanations in a
visualization dashboard, which allows obtaining information about the most and
least liked aspects of similar users obtained from the embeddings of an input
graph.
