IReGNN: Implicit review-enhanced graph neural network for explainable recommendation

Abstract

Explainable recommendations can not only recommend items to users but also provide corresponding explanations, which is crucial for enhancing the transparency, credibility, and security of the system. Reviews, as an important information source for explainable recommendations, have received considerable attention. However, existing review-based explainable recommendations focus primarily on exploring user preferences and item features, as well as generating explanations from reviews, overlooking the limitations imposed by review sparsity on model performance. To address this issue, we propose an Implicit Review-enhanced Graph Neural Network (IReGNN) for explainable recommendations. Specifically, we construct a review network and a rating network, respectively. For the review network, we adopt an unsupervised approach to mine different topics of users and items, thereby enhancing node attribute representations. On the other hand, for the rating network, we extract implicit relationships between individuals and generate virtual reviews under the constraint of topics, which can effectively alleviate the data sparsity issue. Finally, we leverage a spatial graph neural network to learn node representations, generating accurate recommendations and high-quality explanations. Through a series of experiments on three publicly available datasets, results demonstrate that IReGNN outperforms eight baseline models in terms of rating prediction and explanation quality. Moreover, our model also has certain advantages in sparse data scenarios. The model and datasets are released at: https://github.com/SamuelZack/IReGNN.git.