Rating and aspect-based opinion graph embeddings for explainable recommendations
This work addresses the need for more accurate and explainable recommendations in e-commerce and review platforms, though it is incremental as it adapts existing graph embedding techniques.
The paper tackled the problem of improving recommendation systems by proposing a method that uses graph embeddings combining ratings and aspect-based opinions from textual reviews, achieving state-of-the-art performance on Amazon and Yelp datasets across six domains.
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.