Predicting Choice with Set-Dependent Aggregation

Nir Rosenfeld, Kojin Oshiba, Yaron Singer
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:8220-8229, 2020.

Abstract

Providing users with alternatives to choose from is an essential component of many online platforms, making the accurate prediction of choice vital to their success. A renewed interest in learning choice models has led to improved modeling power, but most current methods are either limited in the type of choice behavior they capture, cannot be applied to large-scale data, or both. Here we propose a learning framework for predicting choice that is accurate, versatile, and theoretically grounded. Our key modeling point is that to account for how humans choose, predictive models must be expressive enough to accommodate complex choice patterns but structured enough to retain statistical efficiency. Building on recent results in economics, we derive a class of models that achieves this balance, and propose a neural implementation that allows for scalable end-to-end training. Experiments on three large choice datasets demonstrate the utility of our approach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-rosenfeld20a, title = {Predicting Choice with Set-Dependent Aggregation}, author = {Rosenfeld, Nir and Oshiba, Kojin and Singer, Yaron}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {8220--8229}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/rosenfeld20a/rosenfeld20a.pdf}, url = {https://proceedings.mlr.press/v119/rosenfeld20a.html}, abstract = {Providing users with alternatives to choose from is an essential component of many online platforms, making the accurate prediction of choice vital to their success. A renewed interest in learning choice models has led to improved modeling power, but most current methods are either limited in the type of choice behavior they capture, cannot be applied to large-scale data, or both. Here we propose a learning framework for predicting choice that is accurate, versatile, and theoretically grounded. Our key modeling point is that to account for how humans choose, predictive models must be expressive enough to accommodate complex choice patterns but structured enough to retain statistical efficiency. Building on recent results in economics, we derive a class of models that achieves this balance, and propose a neural implementation that allows for scalable end-to-end training. Experiments on three large choice datasets demonstrate the utility of our approach.} }
Endnote
%0 Conference Paper %T Predicting Choice with Set-Dependent Aggregation %A Nir Rosenfeld %A Kojin Oshiba %A Yaron Singer %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-rosenfeld20a %I PMLR %P 8220--8229 %U https://proceedings.mlr.press/v119/rosenfeld20a.html %V 119 %X Providing users with alternatives to choose from is an essential component of many online platforms, making the accurate prediction of choice vital to their success. A renewed interest in learning choice models has led to improved modeling power, but most current methods are either limited in the type of choice behavior they capture, cannot be applied to large-scale data, or both. Here we propose a learning framework for predicting choice that is accurate, versatile, and theoretically grounded. Our key modeling point is that to account for how humans choose, predictive models must be expressive enough to accommodate complex choice patterns but structured enough to retain statistical efficiency. Building on recent results in economics, we derive a class of models that achieves this balance, and propose a neural implementation that allows for scalable end-to-end training. Experiments on three large choice datasets demonstrate the utility of our approach.
APA
Rosenfeld, N., Oshiba, K. & Singer, Y.. (2020). Predicting Choice with Set-Dependent Aggregation. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:8220-8229 Available from https://proceedings.mlr.press/v119/rosenfeld20a.html.

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