Spectral Methods for Ranking with Scarce Data

Lalit Jain, Anna Gilbert, Umang Varma
Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:609-618, 2020.

Abstract

Given a number of pairwise preferences of items, a common task is to rank all the items.Examples include pairwise movie ratings, New Yorker cartoon caption contests, and many other consumer preferences tasks. What these settings have in common is two-fold: a scarcity of data (it may be costly to get comparisons for all the pairs of items) and additional feature information about the items (e.g., movie genre,director, and cast). In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information. This method returns meaningful rankings even under scarce comparisons.Using diffusion based methods, we incorporate feature information that outperforms state-of-the-art methods in practice. We also provide improved sample complexity for RankCentrality in a variety of sampling schemes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v124-jain20a, title = {Spectral Methods for Ranking with Scarce Data}, author = {Jain, Lalit and Gilbert, Anna and Varma, Umang}, booktitle = {Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI)}, pages = {609--618}, year = {2020}, editor = {Peters, Jonas and Sontag, David}, volume = {124}, series = {Proceedings of Machine Learning Research}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/jain20a/jain20a.pdf}, url = {https://proceedings.mlr.press/v124/jain20a.html}, abstract = {Given a number of pairwise preferences of items, a common task is to rank all the items.Examples include pairwise movie ratings, New Yorker cartoon caption contests, and many other consumer preferences tasks. What these settings have in common is two-fold: a scarcity of data (it may be costly to get comparisons for all the pairs of items) and additional feature information about the items (e.g., movie genre,director, and cast). In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information. This method returns meaningful rankings even under scarce comparisons.Using diffusion based methods, we incorporate feature information that outperforms state-of-the-art methods in practice. We also provide improved sample complexity for RankCentrality in a variety of sampling schemes.} }
Endnote
%0 Conference Paper %T Spectral Methods for Ranking with Scarce Data %A Lalit Jain %A Anna Gilbert %A Umang Varma %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-jain20a %I PMLR %P 609--618 %U https://proceedings.mlr.press/v124/jain20a.html %V 124 %X Given a number of pairwise preferences of items, a common task is to rank all the items.Examples include pairwise movie ratings, New Yorker cartoon caption contests, and many other consumer preferences tasks. What these settings have in common is two-fold: a scarcity of data (it may be costly to get comparisons for all the pairs of items) and additional feature information about the items (e.g., movie genre,director, and cast). In this paper we modify a popular and well studied method, RankCentrality for rank aggregation to account for few comparisons and that incorporates additional feature information. This method returns meaningful rankings even under scarce comparisons.Using diffusion based methods, we incorporate feature information that outperforms state-of-the-art methods in practice. We also provide improved sample complexity for RankCentrality in a variety of sampling schemes.
APA
Jain, L., Gilbert, A. & Varma, U.. (2020). Spectral Methods for Ranking with Scarce Data. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in Proceedings of Machine Learning Research 124:609-618 Available from https://proceedings.mlr.press/v124/jain20a.html.

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