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 para- metric models that generate partial ranks over alternatives given attributes of agents and alter- natives. 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 al- gorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood func- tions 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 para- metric models that generate partial ranks over alternatives given attributes of agents and alter- natives. 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 al- gorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood func- tions 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 para- metric models that generate partial ranks over alternatives given attributes of agents and alter- natives. 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 al- gorithm for MAP inference under GRUMs. We also prove uni-modality of the likelihood func- tions 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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