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

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arxiv 1411.0279 v2 pith:VLKS7XGZ submitted 2014-11-02 cs.DS

Andres Abeliuk , Gerardo Berbeglia , Manuel Cebrian , Pascal Van Hentenryck This is my paper · ORCID
classification cs.DS
keywords influencesocialassortmentoptimalpositioningbiaslogitmodel
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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.

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  1. Position Auctions in AI-Generated Content

    cs.GT 2025-06 conditional novelty 6.0 of 10

    New mechanism-design results for position auctions with context-dependent click-through rates under multinomial logit and cascade user models, with exact optimality in the MNL case and an O(log m) approximation in the...

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