Optimizing Expected Utility in a Multinomial Logit Model with Position Bias and Social Influence
Data Structures and Algorithms
2021-10-01 v2
Abstract
Motivated by applications in retail, online advertising, and cultural markets, this paper studies how to find the optimal assortment and positioning of products subject to a capacity constraint. We prove that the optimal assortment and positioning can be found in polynomial time for a multinomial logit model capturing utilities, position bias, and social influence. Moreover, in a dynamic market, we show that the policy that applies the optimal assortment and positioning and leverages social influence outperforms in expectation any policy not using social influence.
Cite
@article{arxiv.1411.0279,
title = {Optimizing Expected Utility in a Multinomial Logit Model with Position Bias and Social Influence},
author = {Andres Abeliuk and Gerardo Berbeglia and Manuel Cebrian and Pascal Van Hentenryck},
journal= {arXiv preprint arXiv:1411.0279},
year = {2021}
}