RUMs from Head-to-Head Contests

Matteo Almanza, Flavio Chierichetti, Ravi Kumar, Alessandro Panconesi, Andrew Tomkins
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:452-467, 2022.

Abstract

Random utility models (RUMs) encode the likelihood that a particular item will be selected from a slate of competing items. RUMs are well-studied objects in both discrete choice theory and, more recently, in the machine learning community, as they encode a fairly broad notion of rational user behavior. In this paper, we focus on slates of size two representing head-to-head contests. Given a tournament matrix M such that Mi,j is the probability that item j will be selected from {i,j}, we consider the problem of finding the RUM that most closely reproduces M. For this problem we obtain a polynomial-time algorithm returning a RUM that approximately minimizes the average error over the pairs. Our experiments show that RUMs can perfectly represent many of the tournament matrices that have been considered in the literature; in fact, the maximum average error induced by RUMs on the matrices we considered is negligible (≈0.001). We also show that RUMs are competitive, on prediction tasks, with previous approaches.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-almanza22a, title = {{RUM}s from Head-to-Head Contests}, author = {Almanza, Matteo and Chierichetti, Flavio and Kumar, Ravi and Panconesi, Alessandro and Tomkins, Andrew}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {452--467}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/almanza22a/almanza22a.pdf}, url = {https://proceedings.mlr.press/v162/almanza22a.html}, abstract = {Random utility models (RUMs) encode the likelihood that a particular item will be selected from a slate of competing items. RUMs are well-studied objects in both discrete choice theory and, more recently, in the machine learning community, as they encode a fairly broad notion of rational user behavior. In this paper, we focus on slates of size two representing head-to-head contests. Given a tournament matrix $M$ such that $M_{i,j}$ is the probability that item $j$ will be selected from $\{i, j\}$, we consider the problem of finding the RUM that most closely reproduces $M$. For this problem we obtain a polynomial-time algorithm returning a RUM that approximately minimizes the average error over the pairs. Our experiments show that RUMs can perfectly represent many of the tournament matrices that have been considered in the literature; in fact, the maximum average error induced by RUMs on the matrices we considered is negligible ($\approx 0.001$). We also show that RUMs are competitive, on prediction tasks, with previous approaches.} }
Endnote
%0 Conference Paper %T RUMs from Head-to-Head Contests %A Matteo Almanza %A Flavio Chierichetti %A Ravi Kumar %A Alessandro Panconesi %A Andrew Tomkins %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-almanza22a %I PMLR %P 452--467 %U https://proceedings.mlr.press/v162/almanza22a.html %V 162 %X Random utility models (RUMs) encode the likelihood that a particular item will be selected from a slate of competing items. RUMs are well-studied objects in both discrete choice theory and, more recently, in the machine learning community, as they encode a fairly broad notion of rational user behavior. In this paper, we focus on slates of size two representing head-to-head contests. Given a tournament matrix $M$ such that $M_{i,j}$ is the probability that item $j$ will be selected from $\{i, j\}$, we consider the problem of finding the RUM that most closely reproduces $M$. For this problem we obtain a polynomial-time algorithm returning a RUM that approximately minimizes the average error over the pairs. Our experiments show that RUMs can perfectly represent many of the tournament matrices that have been considered in the literature; in fact, the maximum average error induced by RUMs on the matrices we considered is negligible ($\approx 0.001$). We also show that RUMs are competitive, on prediction tasks, with previous approaches.
APA
Almanza, M., Chierichetti, F., Kumar, R., Panconesi, A. & Tomkins, A.. (2022). RUMs from Head-to-Head Contests. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:452-467 Available from https://proceedings.mlr.press/v162/almanza22a.html.

Related Material