232 citations · 1.5k across the 29 of their papers we have counts for
17 papers
Graph Transformer for Recommendation
Chaoliu Li, Lianghao Xia, Xubin Ren +3
This paper presents a novel approach to representation learning in recommender systems by integrating generative self-supervised learning with graph transformer architecture. We hi…
Graph Masked Autoencoder for Sequential Recommendation
Yaowen Ye, Lianghao Xia, Chao Huang
While some powerful neural network architectures (e.g., Transformer, Graph Neural Networks) have achieved improved performance in sequential recommendation with high-order item dep…
Automated Spatio-Temporal Graph Contrastive Learning
Qianru Zhang, Chao Huang, Lianghao Xia +3
Among various region embedding methods, graph-based region relation learning models stand out, owing to their strong structure representation ability for encoding spatial correlati…
POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems
Yixuan Wang, Weichao Zhou, Jiameng Fan +6
Neural networks (NNs) playing the role of controllers have demonstrated impressive empirical performances on challenging control problems. However, the potential adoption of NN con…
Debiased Contrastive Learning for Sequential Recommendation
Yuhao Yang, Chao Huang, Lianghao Xia +3
Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GN…
Graph-less Collaborative Filtering
Lianghao Xia, Chao Huang, Jiao Shi +1
Graph neural networks (GNNs) have shown the power in representation learning over graph-structured user-item interaction data for collaborative filtering (CF) task. However, with t…