activity
20192021
most citedRepresentation Learning for Attributed Multiplex Heterogeneous Network

479 citations · 502 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CL2021

M6: A Chinese Multimodal Pretrainer

Junyang Lin, Rui Men, An Yang +22

In this work, we construct the largest dataset for multimodal pretraining in Chinese, which consists of over 1.9TB images and 292GB texts that cover a wide range of domains. We pro…

cs.IR20217 cited

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…

cs.IR2021

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…

cs.IR202016 cited

Controllable Multi-Interest Framework for Recommendation

Yukuo Cen, Jianwei Zhang, Xu Zou +3

Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequent…

cs.LG2019

Dimensional Reweighting Graph Convolutional Networks

Xu Zou, Qiuye Jia, Jianwei Zhang +3

Graph Convolution Networks (GCNs) are becoming more and more popular for learning node representations on graphs. Though there exist various developments on sampling and aggregatio…

cs.SI2019479 cited

Representation Learning for Attributed Multiplex Heterogeneous Network

Yukuo Cen, Xu Zou, Jianwei Zhang +3

Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and ca…