activity
20192022
most citedOn Node Features for Graph Neural Networks

12 citations · 20 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR2021

PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion

Shijie Zhang, Hongzhi Yin, Tong Chen +3

Due to the growing privacy concerns, decentralization emerges rapidly in personalized services, especially recommendation. Also, recent studies have shown that centralized models a…

cs.IR2021

DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

Lei Guo, Li Tang, Tong Chen +3

Shared-account Cross-domain Sequential recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a…

cs.LG201912 cited

On Node Features for Graph Neural Networks

Chi Thang Duong, Thanh Dat Hoang, Ha The Hien Dang +2

Graph neural network (GNN) is a deep model for graph representation learning. One advantage of graph neural network is its ability to incorporate node features into the learning pr…

cs.LG20191 cited

Sequence-Aware Factorization Machines for Temporal Predictive Analytics

Tong Chen, Hongzhi Yin, Quoc Viet Hung Nguyen +3

In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As…

cs.LG2019

Parallel Computation of Graph Embeddings

Chi Thang Duong, Hongzhi Yin, Thanh Dat Hoang +4

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph propert…