A survey of graph neural networks for social recommender systems
Social recommender systems (SocialRS) simultaneously leverage the user-to-item
interactions as well as the user-to-user social relations for the task of generating item …
interactions as well as the user-to-user social relations for the task of generating item …
A comprehensive survey on multimodal recommender systems: Taxonomy, evaluation, and future directions
Recommendation systems have become popular and effective tools to help users discover
their interesting items by modeling the user preference and item property based on implicit …
their interesting items by modeling the user preference and item property based on implicit …
Dgrec: Graph neural network for recommendation with diversified embedding generation
Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …
more attention in recent years due to their excellent performance in accuracy. Representing …
Empowering collaborative filtering with principled adversarial contrastive loss
Contrastive Learning (CL) has achieved impressive performance in self-supervised learning
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …
tasks, showing superior generalization ability. Inspired by the success, adopting CL into …
Bars: Towards open benchmarking for recommender systems
The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …
recommendation techniques. Despite the significant progress made in both research and …
Learning to denoise unreliable interactions for graph collaborative filtering
Recently, graph neural networks (GNN) have been successfully applied to recommender
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …
systems as an effective collaborative filtering (CF) approach. However, existing GNN-based …
SVD-GCN: A simplified graph convolution paradigm for recommendation
S Peng, K Sugiyama, T Mine - Proceedings of the 31st ACM international …, 2022 - dl.acm.org
With the tremendous success of Graph Convolutional Networks (GCNs), they have been
widely applied to recommender systems and have shown promising performance. However …
widely applied to recommender systems and have shown promising performance. However …
Invariant collaborative filtering to popularity distribution shift
Collaborative Filtering (CF) models, despite their great success, suffer from severe
performance drops due to popularity distribution shifts, where these changes are ubiquitous …
performance drops due to popularity distribution shifts, where these changes are ubiquitous …
To see further: Knowledge graph-aware deep graph convolutional network for recommender systems
Applying a graph convolutional network (GCN) or its variants to user-item interaction graphs
is one of the most commonly used approaches for learning the representation of users and …
is one of the most commonly used approaches for learning the representation of users and …
Hicf: Hyperbolic informative collaborative filtering
Considering the prevalence of the power-law distribution in user-item networks, hyperbolic
space has attracted considerable attention and achieved impressive performance in the …
space has attracted considerable attention and achieved impressive performance in the …