Stochastic Rank Aggregation

Shuzi Niu, Yanyan Lan, Jiafeng Guo, Xueqi Cheng
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:579-588, 2013.

Abstract

This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Tra- ditional rank aggregation methods are deter- ministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surprisingly, experimental results on real data sets show that explicit rank ag- gregation methods would not work as well as implicit methods, although rank information is critical for the task. Our analysis indicates that the major reason might be the unreli- able rank information from incomplete rank- ing inputs. To solve this problem, we propose to incorporate uncertainty into rank aggrega- tion and tackle the problem in both unsuper- vised and supervised scenario. We call this novel framework stochastic rank aggregation (St.Agg for short). Specifically, we introduce a prior distribution on ranks, and transform the ranking functions or objectives in tradi- tional explicit methods to their expectations over this distribution. Our experiments on benchmark data sets show that the proposed St.Agg outperforms the baselines in both un- supervised and supervised scenarios.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-niu13a, title = {Stochastic Rank Aggregation}, author = {Niu, Shuzi and Lan, Yanyan and Guo, Jiafeng and Cheng, Xueqi}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {579--588}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/niu13a/niu13a.pdf}, url = {https://proceedings.mlr.press/r11/niu13a.html}, abstract = {This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Tra- ditional rank aggregation methods are deter- ministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surprisingly, experimental results on real data sets show that explicit rank ag- gregation methods would not work as well as implicit methods, although rank information is critical for the task. Our analysis indicates that the major reason might be the unreli- able rank information from incomplete rank- ing inputs. To solve this problem, we propose to incorporate uncertainty into rank aggrega- tion and tackle the problem in both unsuper- vised and supervised scenario. We call this novel framework stochastic rank aggregation (St.Agg for short). Specifically, we introduce a prior distribution on ranks, and transform the ranking functions or objectives in tradi- tional explicit methods to their expectations over this distribution. Our experiments on benchmark data sets show that the proposed St.Agg outperforms the baselines in both un- supervised and supervised scenarios.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Stochastic Rank Aggregation %A Shuzi Niu %A Yanyan Lan %A Jiafeng Guo %A Xueqi Cheng %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-niu13a %I PMLR %P 579--588 %U https://proceedings.mlr.press/r11/niu13a.html %V R11 %X This paper addresses the problem of rank aggregation, which aims to find a consensus ranking among multiple ranking inputs. Tra- ditional rank aggregation methods are deter- ministic, and can be categorized into explicit and implicit methods depending on whether rank information is explicitly or implicitly utilized. Surprisingly, experimental results on real data sets show that explicit rank ag- gregation methods would not work as well as implicit methods, although rank information is critical for the task. Our analysis indicates that the major reason might be the unreli- able rank information from incomplete rank- ing inputs. To solve this problem, we propose to incorporate uncertainty into rank aggrega- tion and tackle the problem in both unsuper- vised and supervised scenario. We call this novel framework stochastic rank aggregation (St.Agg for short). Specifically, we introduce a prior distribution on ranks, and transform the ranking functions or objectives in tradi- tional explicit methods to their expectations over this distribution. Our experiments on benchmark data sets show that the proposed St.Agg outperforms the baselines in both un- supervised and supervised scenarios. %Z Reissued by PMLR on 04 October 2026.
APA
Niu, S., Lan, Y., Guo, J. & Cheng, X.. (2013). Stochastic Rank Aggregation. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:579-588 Available from https://proceedings.mlr.press/r11/niu13a.html. Reissued by PMLR on 04 October 2026.

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