这里,solidity是指边没有噪声的概率,它在增强任务中的标签是根据学习到的超图依赖关系和表征来计算的。通过这种方式,SHT 将知识从超图Transformer中的高级和去噪特征迁移到低级和嘈杂的拓扑感知embedding,这有助于重新校准局部图结构并提高模型的鲁棒性。流程如图 2 所示。 2.3.1 元网络的 Solidity 标签 在SHT 模型中...
3)尽管超图transformer可以使用可学习的超边连接任何用户/商品,但是高阶迭代仍然通过迭代超图传播提高了模型性能 模型稳健性检验 噪声对于性能的影响 与基线相比,SHT在大多数情况下表现出更小的性能退化,原因有两个:(1)超图transformer的全局关系学习和信息传播缓解了原始观察到的用户-商品交互带来的噪声影响(2)自增强学...
Self-supervised Hypergraph Transformer with Alignment and Uniformity for RecommendationXian Feng YangYang LiuIAENG International Journal of Computer Science
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation (Group Rec, Graph + CL + DA) CIKM 2021, [PDF], [Code] Self-Supervised Hypergraph Transformer for Recommender Systems (Graph + SSL) KDD 2022, [PDF], [Code] Episodes Discovery Recommendation with Multi-Source Augmentations ...
Hypergraph-based Recommender Systems HyRec:将用户视为超边来聚合来自交互商品的信息 MHCN:通过构建多通道超图来建模用户间的高阶关系 DHCF:是一种学习混合高阶相关性的超图协同过滤模型 Self-Supervised Graph Learning DGI和GMI:在带有辅助任务的GNN框架上进行生成式自监督学习 SGL:通过随机的节点和边丢弃操作生成对...
(KDD'2022) Self-Supervised Hypergraph Transformer for Recommender Systems [paper] (SIGIR'2022) Hypergraph Contrastive Collaborative Filtering [paper] (SIGIR'2022) Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering [paper] (SIGIR'2022) Are Graph Augmentations Necessary?: Simple ...
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks Recently, pretraining methods for the Graph Neural Networks (GNNs) have been successful at learning effective representations from unlabeled graph data. Ho... A Abubaker,T Maehara,M Nimishakavi,... 被引量: 0发表: 2023年 HetG...
In heterogeneous graph analysis, existing self-supervised learning (SSL) methods face several key challenges. Primarily, these approaches are tailored for
Hypergraph-based Recommender Systems HyRec：将用户视为超边来聚合来自交互商品的信息 MHCN：通过构建多...
KDD 2022 | Self-Supervised Hypergraph Transformer for Recommender Systems 文章信息「来源」:Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2022)「标题」:Self-Supervised Hypergraph Transformer for Recommender Systems「作者」:Xia, Lianghao and Huang, Chao and Zh...