Hypercomplex Graph Collaborative Filtering
Anchen Li, Bo Yang, Huan Huo, Farookh Khadeer Hussain
Abstract
Hypercomplex algebras are well-developed in the area of mathematics. Recently, several hypercomplex recommendation approaches have been proposed and yielded great success. However, two vital issues have not been well-considered in existing hypercomplex recommenders. First, these methods are only designed for specific and low-dimensional hypercomplex algebras (e.g., complex and quaternion algebras), ignoring the exploration and utilization of high-dimensional ones. Second, most recommenders treat every user-item interaction as an isolated data instance, without considering high-order collaborative relationships.
Ask about this paper
Ask your agent about it.
Lune has read the top-tier papers around this one, so every answer names the papers it rests on.
Cited by top-tier papers1
Ask how each one uses itRelated papers
- Hypercomplex Knowledge Graph-Aware RecommendationAnchen Li, Bo Yang, Huan Huo, Farookh Hussain et al.SIGIR 2025 · 15 citations
- Quaternion-Based Knowledge Graph Network for RecommendationZhaopeng Li, Qianqian Xu, Yangbangyan Jiang, Xiaochun Cao et al.ACM MM 2020 · 30 citations
- Dual Channel Hypergraph Collaborative FilteringShuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao et al.KDD 2020 · 213 citations
- Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social RecommendationJunliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang et al.WWW 2021 · 598 citations
- Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual BanditXian Wu, Suleyman Cetintas, Deguang Kong, Miao Lu et al.WWW 2020 · 8 citations
