Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation

no code implementations • 16 May 2022 • Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma, Kaixin Gao, Bing Han, Lin Zheng, Xiaobo Guo

In this paper, we propose a Poincar\'{e}-based heterogeneous graph neural network named PHGR to model the sequential pattern information as well as hierarchical information contained in the data of SR scenarios simultaneously.

Graph Representation Learning Sequential Recommendation

HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation

no code implementations • 6 Jul 2021 • Naicheng Guo, Xiaolei Liu, Shaoshuai Li, Qiongxu Ma, Yunan Zhao, Bing Han, Lin Zheng, Kaixin Gao, Xiaobo Guo

Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors.

Contrastive Learning Graph Representation Learning +1

THOR, Trace-based Hardware-adaptive layer-ORiented Natural Gradient Descent Computation

no code implementations • AAAI Technical Track on Machine Learning 2021 • Mengyun Chen, Kaixin Gao, Xiaolei Liu, Zidong Wang, Ningxi Ni, Qian Zhang, Lei Chen, Chao Ding, ZhengHai Huang, Min Wang, Shuangling Wang, Fan Yu, Xinyuan Zhao, Dachuan Xu

It is well-known that second-order optimizer can accelerate the training of deep neural networks, however, the huge computation cost of second-order optimization makes it impractical to apply in real practice.