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

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8 papers · 1 filter

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.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…

cs.IR2015★ 50 cited

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…

cs.IR2013★ 19 cited

Information filtering in sparse online systems: recommendation via semi-local diffusion

Wei Zeng, An Zeng, Ming-Sheng Shang +1

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively sup…

cs.IR2013★ 3 cited

Membership in social networks and the application in information filtering

Wei Zeng, An Zeng, Ming-Sheng Shang +1

During the past a few years, users' membership in the online system (i.e. the social groups that online users joined) are wildly investigated. Most of these works focus on the dete…

cs.IR2013★ 62 cited

Extracting the information backbone in online system

Qian-Ming Zhang, An Zeng, Ming-Sheng Shang

Information overload is a serious problem in modern society and many solutions such as recommender system have been proposed to filter out irrelevant information. In the literature…