activity
20092026
most citedEmpirical analysis of web-based user-object bipartite networks

115 citations · 492 across the 17 of their papers we have counts for

collaborators

17 papers

cs.IR2026

Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

Chongjun Xia, Xiaoyu Shi, Hong Xie +3

Item-side fairness is crucial for ensuring the fair exposure of long-tail items in interactive recommender systems. Existing approaches promote the exposure of long-tail items by d…

cs.CV2021★ 3 cited

Activated Gradients for Deep Neural Networks

Mei Liu, Liangming Chen, Xiaohao Du +2

Deep neural networks often suffer from poor performance or even training failure due to the ill-conditioned problem, the vanishing/exploding gradient problem, and the saddle point…

cs.LG2020★ 1 cited

TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrix

Khushnood Abbas, Alireza Abbasi, Dong Shi +4

Understanding the evolutionary patterns of real-world evolving complex systems such as human interactions, transport networks, biological interactions, and computer networks has im…

cs.SI2016★ 8 cited

Rising Novelties on Evolving Networks: Recent Behavior Dominant and Non-Dominant Model

Khushnood Abbas

Novelty attracts attention like popularity. Hence predicting novelty is as important as popularity. Novelty is the side effect of competition and aging in evolving systems. Recent…

cs.SI2016

Identifying emerging influential Nodes in evolving networks: Exploiting strength of weak nodes

Khushnood Abbas, Mingsheng Shang, Cai Shi-Min +1

Identifying emerging influential or popular node/item in future on network is a current interest of the researchers. Most of previous works focus on identifying leaders in time evo…

cs.IR2015★ 9 cited

A Fast Recommendation Algorithm for Social Tagging Systems : A Delicious Case

Yao-Dong Zhao, Shi-Min Cai, Ming Tang +1

The tripartite graph is one of the commonest topological structures in social tagging systems such as Delicious, which has three types of nodes (i.e., users, URLs and tags). Tradit…