Rating and aspect-based opinion graph embeddings for explainable recommendations

TL;DR

This paper introduces a novel graph embedding approach that combines ratings and aspect-based opinions from reviews to improve recommendation accuracy and provide explainability.

Contribution

It proposes a new method leveraging embeddings from combined rating and opinion graphs, enhancing recommendation performance and interpretability.

Findings

Outperforms baseline recommenders on Amazon and Yelp datasets.

Provides explanations based on aspect-based opinions.

Effective across six different domains.

Abstract

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.