  The user review data have been demonstrated to be effective in solving
different recommendation problems. Previous review-based recommendation methods
usually employ sophisticated compositional models, such as Recurrent Neural
Networks (RNN) and Convolutional Neural Networks (CNN), to learn semantic
representations from the review data for recommendation. However, these methods
mainly capture the local dependency between neighbouring words in a word
window, and they treat each review equally. Therefore, they may not be
effective in capturing the global dependency between words, and tend to be
easily biased by noise review information. In this paper, we propose a novel
review-based recommendation model, named Review Graph Neural Network (RGNN).
Specifically, RGNN builds a specific review graph for each individual
user/item, which provides a global view about the user/item properties to help
weaken the biases caused by noise review information. A type-aware graph
attention mechanism is developed to learn semantic embeddings of words.
Moreover, a personalized graph pooling operator is proposed to learn
hierarchical representations of the review graph to form the semantic
representation for each user/item. We compared RGNN with state-of-the-art
review-based recommendation approaches on two real-world datasets. The
experimental results indicate that RGNN consistently outperforms baseline
methods, in terms of Mean Square Error (MSE).
