A Review-aware Graph Contrastive Learning Framework for Recommendation

Understand

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.