许多实际推荐场景中的用户行为数据通常是嘈杂的并且呈现出偏态分布,这可能导致基于 GNN 的模型中的表征性能欠佳。本文提出了 SHT,自监督超图Transformer框架(SHT),它通过以显式方式探索全局协作关系来增强用户表征。具体来说,利用图协同过滤范式使用超图Transformer来维持用户和商品之间的全局协作效果。利用提炼的全局上下文,...
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...
【会话推荐论文阅读】Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation,程序员大本营,技术文章内容聚合第一站。
Xia X, Yin H, Yu J, et al (2020) Self-supervised hypergraph convolutional networks for session-based recommendation https://doi.org/10.13140/RG.2.2.13606.75846 Cai D, Qian S, Fang Q, Hu J, Ding W, Xu C (2023) Heterogeneous graph contrastive learning network for personalized micro-video ...
Yang, F., Peng, D., Zhang, S.: Self-supervised hypergraph neural network for session-based recommendation supported by user continuous topic intent. Appl. Soft Comput. 111406 (2024) Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., Xie, X.: Self-supervised graph learn...
Hypergraph Contrastive Collaborative Filtering (Graph + CF + CL + DA) SIGIR 2022, [PDF], [Code] Graph Structure Aware Contrastive Knowledge Distillation for Incremental Learning in Recommender Systems (Graph + CL) CIKM 2021, [PDF], [Code] Double-Scale Self-Supervised Hypergraph Learning for Group...
DHCN:Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation 长腿冯宝宝 11 人赞同了该文章 摘要:在交互序列中进行特征捕捉对用户的兴趣建模无疑是重要的,但目前的研究大都停留在项目对上的关系特征捕捉,而忽略了序列中项目间的高阶信息。而超图的性质能够很好地解决这个问题。 超图定义 ...
“Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation” CIKM2021 研究问题 群组推荐 研究现状&GAP 传统方法通常采用启发式或基于注意力的偏好聚合策略来综合群体偏好,但这些模型主要关注用户之间的成对连接,忽略了群体内外的复杂高阶交互。\此外,由于群体-物品交互严重稀疏,群体推荐还面临数据稀疏...
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discrimination strategy, suffering from two significant limitations: (1) ...
Moreover, to enhance hypergraph modeling, we devise another graph convolutional network which is based on the line graph of the hypergraph and then integrate self-supervised learning into the training of the networks by maximizing mutual information between the session representations learned via the ...