IncVNM: An Interactive Recommender System with Incremental von Neumann-Morgenstern Utility Learning
Abstract
In modern recommender systems, users are often presented with products whose outcomes are random. The values of such products depend on their entire outcome distributions and on users' attitudes toward risk, making them difficult to evaluate using traditional product-level recommendation methods. We propose \textsc{IncVNM}, an interactive recommender system that incrementally learns a user-specific von Neumann--Morgenstern (VNM) utility function from ordinal click feedback generated during ordinary webpage browsing. At each interaction, \textsc{IncVNM} converts the click order within a displayed product list into pairwise comparisons between outcome distributions and updates the nominal utility by minimizing its deviation from the previous estimate while incorporating the newly observed pairwise comparisons. To close the interaction loop, an entropy-regularized sampling policy balances exploitation of the learned utility against exploration for additional preference information. We evaluate \textsc{IncVNM} in a rolling-window experiment with different response-error probabilities and show that it substantially outperforms the expected-return, mean--variance, discounted-UCB, and distribution-aware BPR baselines.
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BibTeX
@misc{wei2026incvnm,
title = {{IncVNM: An Interactive Recommender System with Incremental von Neumann-Morgenstern Utility Learning}},
author = {Jiaxin Wei and Jia Liu},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.01786},
eprint = {2611.01786},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.01786},
}
RIS
TY - UNPB
TI - IncVNM: An Interactive Recommender System with Incremental von Neumann-Morgenstern Utility Learning
AU - Jiaxin Wei
AU - Jia Liu
PY - 2026
DA - 2026/11/01
PB - arXiv
DO - 10.48550/arxiv.2611.01786
UR - https://arxiv.org/abs/2611.01786
AB - In modern recommender systems, users are often presented with products whose outcomes are random. The values of such products depend on their entire outcome distributions and on users' attitudes toward risk, making them difficult to evaluate using traditional product-level recommendation methods. We propose \textsc{IncVNM}, an interactive recommender system that incrementally learns a user-specific von Neumann--Morgenstern (VNM) utility function from ordinal click feedback generated during ordinary webpage browsing. At each interaction, \textsc{IncVNM} converts the click order within a displayed product list into pairwise comparisons between outcome distributions and updates the nominal utility by minimizing its deviation from the previous estimate while incorporating the newly observed pairwise comparisons. To close the interaction loop, an entropy-regularized sampling policy balances exploitation of the learned utility against exploration for additional preference information. We evaluate \textsc{IncVNM} in a rolling-window experiment with different response-error probabilities and show that it substantially outperforms the expected-return, mean--variance, discounted-UCB, and distribution-aware BPR baselines.
ER -