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The success of neural network embeddings has entailed a renewed interest inusing knowledge graphs for a wide variety of machine learning and informationretrieval tasks. In particular, current recommendation methods based on graphembeddings have shown state-of-the-art performance. These methods commonlyencode latent rating patterns and content features. Different from previouswork, in this paper, we propose to exploit embeddings extracted from graphsthat combine information from ratings and aspect-based opinions expressed intextual reviews. We then adapt and evaluate state-of-the-art graph embeddingtechniques over graphs generated from Amazon and Yelp reviews on six domains,outperforming baseline recommenders. Our approach has the advantage ofproviding explanations which leverage aspect-based opinions given by usersabout recommended items. Furthermore, we also provide examples of theapplicability of recommendations utilizing aspect opinions as explanations in avisualization dashboard, which allows obtaining information about the most andleast liked aspects of similar users obtained from the embeddings of an inputgraph.

Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations