activity
20202022
most citedFederated Graph Learning -- A Position Paper

22 citations · 47 across the 5 of their papers we have counts for

collaborators

5 papers

cs.IR20222 cited

Deep Unified Representation for Heterogeneous Recommendation

Chengqiang Lu, Mingyang Yin, Shuheng Shen +3

Recommendation system has been a widely studied task both in academia and industry. Previous works mainly focus on homogeneous recommendation and little progress has been made for…

cs.IR202116 cited

Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation

Yongji Wu, Defu Lian, Neil Zhenqiang Gong +4

Self-attention has become increasingly popular in a variety of sequence modeling tasks from natural language processing to recommendation, due to its effectiveness. However, self-a…

cs.IR20216 cited

Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention

Yongji Wu, Lu Yin, Defu Lian +4

Sequential recommendation plays an increasingly important role in many e-commerce services such as display advertisement and online shopping. With the rapid development of these se…

cs.LG202122 cited

Federated Graph Learning -- A Position Paper

Huanding Zhang, Tao Shen, Fei Wu +3

Graph neural networks (GNN) have been successful in many fields, and derived various researches and applications in real industries. However, in some privacy sensitive scenarios (l…

cs.SE20201 cited

A Quantitative Study of Security Bug Fixes of GitHub Repositories

Daito Nakano, Mingyang Yin, Ryosuke Sato +3

Software is prone to bugs and failures. Security bugs are those that expose or share privileged information and access in violation of the software's requirements. Given the seriou…