activity
20182022
most citedMultiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

145 citations · 493 across the 18 of their papers we have counts for

collaborators

24 papers

cs.CV20221 cited

Compressing Volumetric Radiance Fields to 1 MB

Lingzhi Li, Zhen Shen, Zhongshu Wang +2

Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast trai…

cs.CV2022

A-ACT: Action Anticipation through Cycle Transformations

Akash Gupta, Jingen Liu, Liefeng Bo +2

While action anticipation has garnered a lot of research interest recently, most of the works focus on anticipating future action directly through observed visual cues only. In thi…

cs.LG20228 cited

An Efficient and Robust System for Vertically Federated Random Forest

Houpu Yao, Jiazhou Wang, Peng Dai +2

As there is a growing interest in utilizing data across multiple resources to build better machine learning models, many vertically federated learning algorithms have been proposed…

cs.IR202280 cited

Multi-Behavior Enhanced Recommendation with Cross-Interaction Collaborative Relation Modeling

Lianghao Xia, Chao Huang, Yong Xu +3

Many previous studies aim to augment collaborative filtering with deep neural network techniques, so as to achieve better recommendation performance. However, most existing deep le…

cs.LG202121 cited

Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network

Xiyue Zhang, Chao Huang, Yong Xu +5

Accurate forecasting of citywide traffic flow has been playing critical role in a variety of spatial-temporal mining applications, such as intelligent traffic control and public ri…

cs.IR2021145 cited

Multiplex Behavioral Relation Learning for Recommendation via Memory Augmented Transformer Network

Lianghao Xia, Chao Huang, Yong Xu +3

Capturing users' precise preferences is of great importance in various recommender systems (eg., e-commerce platforms), which is the basis of how to present personalized interestin…