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Yifeng Zeng
Researcher at Northumbria University
Publications - 159
Citations - 2002
Yifeng Zeng is an academic researcher from Northumbria University. The author has contributed to research in topics: Influence diagram & Computer science. The author has an hindex of 17, co-authored 139 publications receiving 1597 citations. Previous affiliations of Yifeng Zeng include Washington University in St. Louis & Xiamen University.
Papers
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Proceedings Article
Personalized ranking metric embedding for next new POI recommendation
TL;DR: This paper proposes a personalized ranking metric embedding method (PRME) to model personalized check-in sequences and develops a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance.
Proceedings ArticleDOI
Time Constrained Influence Maximization in Social Networks
TL;DR: It is shown that the problem is NP-hard, and the monotonicity and submodularity of the time constrained influence spread function is proved, and a greedy algorithm with performance guarantees is developed.
Journal ArticleDOI
Graphical models for interactive POMDPs: representations and solutions
TL;DR: New graphical representations for the problem of sequential decision making in partially observable multiagent environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs), and the error bound of the approximation technique are discussed and demonstrated.
Journal ArticleDOI
Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond
TL;DR: It is shown that the problem is NP-hard, and the monotonicity and submodularity of the time constrained influence spread function is proved, and a greedy algorithm is developed based on this, which is developed to improve the algorithm scalability.
Journal ArticleDOI
Mathematical programming methods for consistency and consensus in group decision making with intuitionistic fuzzy preference relations
TL;DR: A novel method for checking and improving the consistency of individual IFPRs and the consensus among experts and an attractive property is proved that the collective IFPR is acceptable consistent if all individualIFPRs are acceptable consistent.