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 cascade case.
Optimizing Expected Utility in a Multinomial Logit Model with Position Bias and Social Influence
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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.
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Position Auctions in AI-Generated Content
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 cascade case.