2 citations · 4 across the 9 of their papers we have counts for
9 papers
Graph Neural Patching for Cold-Start Recommendations
Hao Chen, Yu Yang, Yuanchen Bei +3
The cold start problem in recommender systems remains a critical challenge. Current solutions often train hybrid models on auxiliary data for both cold and warm users/items, potent…
Macro Graph Neural Networks for Online Billion-Scale Recommender Systems
Hao Chen, Yuanchen Bei, Qijie Shen +6
Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational comp…
Multi-factor Sequential Re-ranking with Perception-Aware Diversification
Yue Xu, Hao Chen, Zefan Wang +8
Feed recommendation systems, which recommend a sequence of items for users to browse and interact with, have gained significant popularity in practical applications. In feed produc…
Multi-channel Integrated Recommendation with Exposure Constraints
Yue Xu, Qijie Shen, Jianwen Yin +6
Integrated recommendation, which aims at jointly recommending heterogeneous items from different channels in a main feed, has been widely applied to various online platforms. Thoug…
Flattened Graph Convolutional Networks For Recommendation
Yue Xu, Hao Chen, Zengde Deng +2
Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform rec…
GPatch: Patching Graph Neural Networks for Cold-Start Recommendations
Hao Chen, Zefan Wang, Yue Xu +2
Cold start is an essential and persistent problem in recommender systems. State-of-the-art solutions rely on training hybrid models for both cold-start and existing users/items, ba…
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation
Hao Chen, Zhong Huang, Yue Xu +4
The recently proposed Graph Convolutional Networks (GCNs) have achieved significantly superior performance on various graph-related tasks, such as node classification and recommend…
Learning Single/Multi-Attribute of Object with Symmetry and Group
Yong-Lu Li, Yue Xu, Xinyu Xu +2
Attributes and objects can compose diverse compositions. To model the compositional nature of these concepts, it is a good choice to learn them as transformations, e.g., coupling a…
Non-Recursive Graph Convolutional Networks
Hao Chen, Zengde Deng, Yue Xu +1
Graph Convolutional Networks (GCNs) are powerful models for node representation learning tasks. However, the node representation in existing GCN models is usually generated by perf…