Search Results for author:
Found 6 papers, 6 papers with code
Vector Quantization for Recommender Systems: A Review and Outlook
Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems.
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy
To this end, semi-supervised domain adaptation (SSDA) on graphs aims to leverage the knowledge of a labeled source graph to aid in node classification on a target graph with limited labels.
Learning Category Trees for ID-Based Recommendation: Exploring the Power of Differentiable Vector Quantization
Category information plays a crucial role in enhancing the quality and personalization of recommender systems.
Domain Adaptive Graph Infomax via Conditional Adversarial Networks
The emerging graph neural networks (GNNs) have demonstrated impressive performance on the node classification problem in complex networks.
Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs
The high cost of data labeling often results in node label shortage in real applications.
Graph Transfer Learning via Adversarial Domain Adaptation with Graph Convolution
Existing methods for single network learning cannot solve this problem due to the domain shift across networks.
