Graph Cross-Correlated Network for Recommendation

TL;DR

This paper introduces GCR, a novel graph neural network-based recommendation model that explicitly models correlations between user and item subgraphs, leading to improved prediction performance.

Contribution

The paper proposes GCR, which explicitly captures cross-correlations between user and item subgraph representations, enhancing the semantic understanding in recommendation systems.

Findings

GCR outperforms state-of-the-art models on interaction prediction.

GCR improves click-through rate prediction accuracy.

Explicit modeling of subgraph correlations enhances recommendation quality.

Abstract

Collaborative filtering (CF) models have demonstrated remarkable performance in recommender systems, which represent users and items as embedding vectors. Recently, due to the powerful modeling capability of graph neural networks for user-item interaction graphs, graph-based CF models have gained increasing attention. They encode each user/item and its subgraph into a single super vector by combining graph embeddings after each graph convolution. However, each hop of the neighbor in the user-item subgraphs carries a specific semantic meaning. Encoding all subgraph information into single vectors and inferring user-item relations with dot products can weaken the semantic information between user and item subgraphs, thus leaving untapped potential. Exploiting this untapped potential provides insight into improving performance for existing recommendation models. To this end, we propose the Graph Cross-correlated Network for Recommendation (GCR), which serves as a general recommendation paradigm that explicitly considers correlations between user/item subgraphs. GCR first introduces the Plain Graph Representation (PGR) to extract information directly from each hop of neighbors into corresponding PGR vectors. Then, GCR develops Cross-Correlated Aggregation (CCA) to construct possible cross-correlated terms between PGR vectors of user/item subgraphs. Finally, GCR comprehensively incorporates the cross-correlated terms for recommendations. Experimental results show that GCR outperforms state-of-the-art models on both interaction prediction and click-through rate prediction tasks.