Learning shared rankings from mixtures of noisy pairwise comparisons

Weicong Ding; Prakash Ishwar; Venkatesh Saligrama

ICASSP 2015accepted0 citations

Learning shared rankings from mixtures of noisy pairwise comparisons

Weicong Ding, Prakash Ishwar, Venkatesh Saligrama

Abstract

We propose a novel model for rank aggregation from pairwise comparisons which accounts for a heterogeneous population of inconsistent users whose preferences are different mixtures of multiple shared ranking schemes. By connecting this problem to recent advances in the non-negative matrix factorization (NMF) literature, we develop an algorithm that can learn the underlying shared rankings with provable statistical and computational efficiency guarantees. We validate the approach using semi-synthetic and real world datasets.

BibTeX

@inproceedings{icassp2015_learningsharedra,
  title = {Learning shared rankings from mixtures of noisy pairwise comparisons},
  author = {Weicong Ding and Prakash Ishwar and Venkatesh Saligrama},
  booktitle = {ICASSP 2015},
  year = {2015}
}