Rating and aspect-based opinion graph embeddings for explainable recommendations

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 aspectbased 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.

To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering

“…As artificial neural networks have advanced, researchers have looked into employing deep learning approaches to extract aspects and improve RSs [44][45][46][47]. For example, Da'u et al [45] presented a model that employs a deep learning technique for extracting aspect-sentiment pairs to improve the accuracy of the recommendations.…”