  Most modern recommender systems predict users preferences with two
components: user and item embedding learning, followed by the user-item
interaction modeling. By utilizing the auxiliary review information accompanied
with user ratings, many of the existing review-based recommendation models
enriched user/item embedding learning ability with historical reviews or better
modeled user-item interactions with the help of available user-item target
reviews. Though significant progress has been made, we argue that current
solutions for review-based recommendation suffer from two drawbacks. First, as
review-based recommendation can be naturally formed as a user-item bipartite
graph with edge features from corresponding user-item reviews, how to better
exploit this unique graph structure for recommendation? Second, while most
current models suffer from limited user behaviors, can we exploit the unique
self-supervised signals in the review-aware graph to guide two recommendation
components better? To this end, in this paper, we propose a novel Review-aware
Graph Contrastive Learning (RGCL) framework for review-based recommendation.
Specifically, we first construct a review-aware user-item graph with
feature-enhanced edges from reviews, where each edge feature is composed of
both the user-item rating and the corresponding review semantics. This graph
with feature-enhanced edges can help attentively learn each neighbor node
weight for user and item representation learning. After that, we design two
additional contrastive learning tasks (i.e., Node Discrimination and Edge
Discrimination) to provide self-supervised signals for the two components in
recommendation process. Finally, extensive experiments over five benchmark
datasets demonstrate the superiority of our proposed RGCL compared to the
state-of-the-art baselines.

Information Retrieval