Paper Info
Title
Rank Aggregation: New Bounds for MCx.
Abstract
The rank aggregation problem has received significant recent attention within the computer science community. Its applications today range far beyond the original aim of building metasearch engines to problems in machine learning, recommendation systems and more. Several algorithms have been proposed for these problems, and in many cases approximation guarantees have been proven for them. However, it is also known that some Markov chain based algorithms (MC1, MC2, MC3, MC4) perform extremely well in practice, yet had no known performance guarantees. We prove supra-constant lower bounds on approximation guarantees for all of them. Nevertheless, we show that in particular ways, MC4 can be seen as a generalization of Copeland score.
Year
DOI
Venue
2019
10.1016/j.dam.2017.07.020
Discrete Applied Mathematics
Keywords
DocType
Volume
Rank aggregation,Computations on discrete structures,Combinatorial algorithms,Approximation guarantees
Journal
252
ISSN
Citations 
PageRank 
0166-218X
1
0.35
References 
Authors
6
2
Name
Order
Citations
PageRank
Daniel Freund1235.58
David P. Williamson23564413.34