Optimizing Expected Utility in a Multinomial Logit Model with Position Bias and Social Influence

TL;DR

This paper develops a polynomial-time method to optimize product assortment and positioning in markets with social influence and position bias, demonstrating improved outcomes when leveraging social influence in dynamic settings.

Contribution

It introduces a polynomial-time algorithm for optimal assortment and positioning under social influence and position bias, and shows the benefits of using social influence in dynamic markets.

Findings

Optimal assortment and positioning can be computed efficiently.

Leveraging social influence improves expected market performance.

Policy using social influence outperforms non-influential policies in dynamic markets.

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.