Graph Neural Transport Networks with Non-local Attentions for Recommender Systems
Huiyuan Chen, Chin‐Chia Michael Yeh, Fei Wang, Hao Yang
Abstract
Graph Neural Networks (GNNs) have emerged as powerful tools for collaborative filtering. A key challenge of recommendations is to distill long-range collaborative signals from user-item graphs. Typically, GNNs generate embeddings of users/items by propagating and aggregating the messages between local neighbors. Thus, the ability of GNNs to capture long-range dependencies heavily depends on their depths. However, simply training deep GNNs has several bottleneck effects, e.g., over-fitting & over-smoothing, which may lead to unexpected results if GNNs are not well regularized.
Topics & Concepts
BottleneckCollaborative filteringRecommender systemComputer scienceGraphRange (aeronautics)Artificial neural networkArtificial intelligenceMachine learningSmoothingKey (lock)Deep neural networksTheoretical computer scienceMaterials scienceComputer visionComputer securityComposite materialEmbedded systemRecommender Systems and TechniquesAdvanced Graph Neural NetworksTopic Modeling