32 citations · 89 across the 8 of their papers we have counts for
8 papers
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Daochen Zha, Louis Feng, Qiaoyu Tan +6
We study embedding table placement for distributed recommender systems, which aims to partition and place the tables on multiple hardware devices (e.g., GPUs) to balance the comput…
Graph Contrastive Learning with Personalized Augmentation
Xin Zhang, Qiaoyu Tan, Xiao Huang +1
Graph contrastive learning (GCL) has emerged as an effective tool for learning unsupervised representations of graphs. The key idea is to maximize the agreement between two augment…
MGAE: Masked Autoencoders for Self-Supervised Learning on Graphs
Qiaoyu Tan, Ninghao Liu, Xiao Huang +3
We introduce a novel masked graph autoencoder (MGAE) framework to perform effective learning on graph structure data. Taking insights from self-supervised learning, we randomly mas…
Dynamic Memory based Attention Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Ninghao Liu +4
Sequential recommendation has become increasingly essential in various online services. It aims to model the dynamic preferences of users from their historical interactions and pre…
Sparse-Interest Network for Sequential Recommendation
Qiaoyu Tan, Jianwei Zhang, Jiangchao Yao +4
Recent methods in sequential recommendation focus on learning an overall embedding vector from a user's behavior sequence for the next-item recommendation. However, from empirical…
Learning to Hash with Graph Neural Networks for Recommender Systems
Qiaoyu Tan, Ninghao Liu, Xing Zhao +3
Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects…