TY - GEN
T1 - A robust Bayesian estimation approach for the imprecise Plackett-Luce model
AU - Basu, Tathagata
AU - Destercke, Sébastien
AU - Quost, Benjamin
PY - 2022/7/4
Y1 - 2022/7/4
N2 - Learning to rank has become an important part in the fields of machine learning and statistical learning. Rankings are indeed present in many applications, including cognitive psychology, recommender systems, sports tournament or automated algorithm choices. Rankings are however prone to subjectivity (when provided by users) and to incompleteness (when a contestant is missing, or users only report partial preferences). Robust or cautious approaches may overcome such issues. In this paper, we develop a Bayesian robust approach for a commonly used parametric model, the Plackett-Luce (PL) model. This allows us to obtain interval-valued parameter estimates for the strength parameter of the Plackett-Luce model. We illustrate our method with both synthetic and real data to show the usefulness of skeptic inference.
AB - Learning to rank has become an important part in the fields of machine learning and statistical learning. Rankings are indeed present in many applications, including cognitive psychology, recommender systems, sports tournament or automated algorithm choices. Rankings are however prone to subjectivity (when provided by users) and to incompleteness (when a contestant is missing, or users only report partial preferences). Robust or cautious approaches may overcome such issues. In this paper, we develop a Bayesian robust approach for a commonly used parametric model, the Plackett-Luce (PL) model. This allows us to obtain interval-valued parameter estimates for the strength parameter of the Plackett-Luce model. We illustrate our method with both synthetic and real data to show the usefulness of skeptic inference.
KW - Bayesian analysis
KW - imprecise probability
KW - Plackett-Luce model
KW - preference learning
U2 - 10.1007/978-3-031-08971-8_61
DO - 10.1007/978-3-031-08971-8_61
M3 - Conference contribution book
AN - SCOPUS:85135072198
SN - 9783031089701
T3 - Communications in Computer and Information Science
SP - 757
EP - 769
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems
A2 - Ciucci, Davide
A2 - Couso, Inés
A2 - Medina, Jesús
A2 - Ślęzak, Dominik
A2 - Petturiti, Davide
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer
CY - Cham, Switzerland
T2 - 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2022
Y2 - 11 July 2022 through 15 July 2022
ER -