TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems. (arXiv:2308.14355v2 [cs.LG] UPDATED)

Graph Neural Networks (GNNs) have emerged as promising solutions for
collaborative filtering (CF) through the modeling of user-item interaction
graphs. The nucleus of existing GNN-based recommender systems involves
recursive message passing along user-item interaction edges to refine encoded
embeddings. Despite their demonstrated effectiveness, current GNN-based methods
encounter challenges of limited receptive fields and the presence of noisy
``interest-irrelevant'' connections. In contrast, Transformer-based methods
excel in aggregating information adaptively and globally. Nevertheless, their
application to large-scale interaction graphs is hindered by …

arxiv collaborative collaborative filtering cs.lg embeddings filtering gnn gnns graph graph neural networks graphs modeling networks neural networks power recommender systems recursive refine solutions systems through transformers