Reshaping Graph Recommendation with Edge Graph Collaborative Filtering and Customer Reviews

Graph collaborative filtering approaches learn refined users’ and items’ node representations by iteratively aggregating the informative content (called messages) coming from neighbor nodes into each ego node. Unfortunately, not all interactions (i.e., graph edges) may be equally important to the users and items involved. As this indiscriminate message aggregation leads to multi-hop representation errors, recent strategies have used attention mechanisms to weight neighbors’ importance to the ego node. Despite their success, such solutions seem to disregard the potentially critical impact users’ reviews may play on this weighting process. Reviews convey the multi-faceted user’s opinion about items and provide a fundamental tool to group like-minded customers. In this work, we first formally show the causes of node error representation in graph collaborative filtering and demonstrate how existing neighborhood weighting procedures (e.g., attention mechanisms) may alleviate the issue at the expense of limited hop exploration. Second, we correct the representation error through an additional graph network where we enrich graph edge embeddings through opinion-aware review embeddings to smooth each neighbor node’s importance on its ego node. We call our solution Edge Graph Collaborative Filtering (EGCF). Extensive experiments on three e-commerce datasets show that EGCF competes successfully with traditional, graph- and review-based approaches on accuracy and beyond-accuracy objectives, while a study on the number of explored hops justifies the adopted configuration for EGCF. Code and datasets are available at: https://github.com/sisinflab/Edge-Graph-Collaborative-Filtering.