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Found 9 papers, 1 papers with code
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Recent advances have utilized Graph Neural Networks (GNNs) to learn effective node representations by aggregating information from neighborhoods.
Large Language Model Interaction Simulator for Cold-Start Item Recommendation
To address this challenge, we propose an LLM Interaction Simulator (LLM-InS) to model users' behavior patterns based on the content aspect.
Multi-Behavior Collaborative Filtering with Partial Order Graph Convolutional Networks
POG defines the partial order relation of multiple behaviors and models behavior combinations as weighted edges to merge separate behavior graphs into a joint POG.
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors.
Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection
To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).
Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering
To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering.
Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation
(2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times.
CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks
Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world.
Flattened Graph Convolutional Networks For Recommendation
Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
