173 citations · 1.2k across the 29 of their papers we have counts for
7 papers · 1 filter
Improving personalized link prediction by hybrid diffusion
Jin-Hu Liu, Yu-Xiao Zhu, Tao Zhou
Inspired by traditional link prediction and to solve the problem of recommending friends in social networks, we introduce the personalized link prediction in this paper, in which e…
A vertex similarity index for better personalized recommendation
Ling-Jiao Chen, Zi-Ke Zhang, Jin-Hu Liu +2
Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized re…
Online Social Activity Reflects Economic Status
Jin-Hu Liu, Jun Wang, Junming Shao +1
To characterize economic development and diagnose the economic health condition, several popular indices such as gross domestic product (GDP), industrial structure and income growt…
Evaluating user reputation in online rating systems via an iterative group-based ranking method
Jian Gao, Tao Zhou
Reputation is a valuable asset in online social lives and it has drawn increased attention. How to evaluate user reputation in online rating systems is especially significant due t…
Promotion and resignation in employee networks
Jia Yuan, Qian-Ming Zhang, Jian Gao +4
Enterprises have put more and more emphasis on data analysis so as to obtain effective management advices. Managers and researchers are trying to dig out the major factors that lea…
Consistence beats causality in recommender systems
Xuzhen Zhu, Hui Tian, Zheng Hu +2
The explosive growth of information challenges people's capability in finding out items fitting to their own interests. Recommender systems provide an efficient solution by automat…
Group-based ranking method for online rating systems with spamming attacks
Jian Gao, Yu-Wei Dong, Mingsheng Shang +2
Ranking problem has attracted much attention in real systems. How to design a robust ranking method is especially significant for online rating systems under the threat of spamming…