7 citations · 10 across the 5 of their papers we have counts for
Showing cs.IRShow all
3 papers · 1 filter
cs.IR2024
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Shaowei Wei, Zhengwei Wu, Xin Li +5
Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. T…
cs.IR2023
Long-tail Augmented Graph Contrastive Learning for Recommendation
Qian Zhao, Zhengwei Wu, Zhiqiang Zhang +1
Graph Convolutional Networks (GCNs) has demonstrated promising results for recommender systems, as they can effectively leverage high-order relationship. However, these methods usu…
cs.IR2023★ 3 cited
Generative Contrastive Graph Learning for Recommendation
Yonghui Yang, Zhengwei Wu, Le Wu +5
By treating users' interactions as a user-item graph, graph learning models have been widely deployed in Collaborative Filtering(CF) based recommendation. Recently, researchers hav…