Preference Elicitation For General Random Utility Models

Hossein Azari Soufiani, David Parkes, Lirong Xia
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:648-657, 2013.

Abstract

This paper discusses General Random Utility Models (GRUMs). These are a class of parametric models that generate partial ranks over alternatives given attributes of agents and alternatives. We propose two preference elicitation scheme for GRUMs developed from principles in Bayesian experimental design, one for social choice and the other for personalized choice. We couple this with a general Monte-Carlo- Expectation-Maximization (MC-EM) based algorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood functions for a class of GRUMs. We examine the performance of various criteria by experimental studies, which show that the proposed elicitation scheme increases the precision of estimation.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-soufiani13a, title = {Preference Elicitation For General Random Utility Models}, author = {Soufiani, Hossein Azari and Parkes, David and Xia, Lirong}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {648--657}, 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/soufiani13a/soufiani13a.pdf}, url = {https://proceedings.mlr.press/r11/soufiani13a.html}, abstract = {This paper discusses General Random Utility Models (GRUMs). These are a class of parametric models that generate partial ranks over alternatives given attributes of agents and alternatives. We propose two preference elicitation scheme for GRUMs developed from principles in Bayesian experimental design, one for social choice and the other for personalized choice. We couple this with a general Monte-Carlo- Expectation-Maximization (MC-EM) based algorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood functions for a class of GRUMs. We examine the performance of various criteria by experimental studies, which show that the proposed elicitation scheme increases the precision of estimation.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Preference Elicitation For General Random Utility Models %A Hossein Azari Soufiani %A David Parkes %A Lirong Xia %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-soufiani13a %I PMLR %P 648--657 %U https://proceedings.mlr.press/r11/soufiani13a.html %V R11 %X This paper discusses General Random Utility Models (GRUMs). These are a class of parametric models that generate partial ranks over alternatives given attributes of agents and alternatives. We propose two preference elicitation scheme for GRUMs developed from principles in Bayesian experimental design, one for social choice and the other for personalized choice. We couple this with a general Monte-Carlo- Expectation-Maximization (MC-EM) based algorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood functions for a class of GRUMs. We examine the performance of various criteria by experimental studies, which show that the proposed elicitation scheme increases the precision of estimation. %Z Reissued by PMLR on 04 October 2026.
APA
Soufiani, H.A., Parkes, D. & Xia, L.. (2013). Preference Elicitation For General Random Utility Models. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:648-657 Available from https://proceedings.mlr.press/r11/soufiani13a.html. Reissued by PMLR on 04 October 2026.

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