2 citations · 3 across the 4 of their papers we have counts for
4 papers
An Industrial-Scale Sequential Recommender for LinkedIn Feed Ranking
Lars Hertel, Gaurav Srivastava, Syed Ali Naqvi +21
LinkedIn Feed enables professionals worldwide to discover relevant content, build connections, and share knowledge at scale. We present Feed Sequential Recommender (Feed SR), a tra…
Distance-aware Self-adaptive Graph Convolution for Fine-grained Hierarchical Recommendation
Tao Huang, Yihong Chen, Wei Fan +2
Graph Convolutional Networks (GCNs) are widely used to improve recommendation accuracy and performance by effectively learning the representations of user and item nodes. However,…
Contrastive Learning Method for Sequential Recommendation based on Multi-Intention Disentanglement
Zeyu Hu, Yuzhi Xiao, Tao Huang +1
Sequential recommendation is one of the important branches of recommender system, aiming to achieve personalized recommended items for the future through the analysis and predictio…
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang +5
Deep learning has achieved remarkable success in graph-related tasks, yet this accomplishment heavily relies on large-scale high-quality annotated datasets. However, acquiring such…