10 citations · 17 across the 12 of their papers we have counts for
6 papers · 1 filter
Lossless and Privacy-Preserving Graph Convolution Network for Federated Item Recommendation
Guowei Wu, Weike Pan, Qiang Yang +1
Graph neural network (GNN) has emerged as a state-of-the-art solution for item recommendation. However, existing GNN-based recommendation methods rely on a centralized storage of f…
A Survey on Sequential Recommendation
Liwei Pan, Weike Pan, Meiyan Wei +2
Different from most conventional recommendation problems, sequential recommendation focuses on learning users' preferences by exploiting the internal order and dependency among the…
Federated Graph Learning for Cross-Domain Recommendation
Ziqi Yang, Zhaopeng Peng, Zihui Wang +6
Cross-domain recommendation (CDR) offers a promising solution to the data sparsity problem by enabling knowledge transfer across source and target domains. However, many recent CDR…
Sample Enrichment via Temporary Operations on Subsequences for Sequential Recommendation
Shu Chen, Jinwei Luo, Weike Pan +3
Sequential recommendation leverages interaction sequences to predict forthcoming user behaviors, crucial for crafting personalized recommendations. However, the true preferences of…
Delving into Differentially Private Transformer
Youlong Ding, Xueyang Wu, Yining Meng +3
Deep learning with differential privacy (DP) has garnered significant attention over the past years, leading to the development of numerous methods aimed at enhancing model accurac…
A Survey on Cross-Domain Sequential Recommendation
Shu Chen, Zitao Xu, Weike Pan +2
Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple do…