Timezone: »
Many online platforms act as intermediaries between a seller and a set of buyers. Examples of such settings include online retailers (such as Ebay) selling items on behalf of sellers to buyers, or advertising exchanges (such as AdX) selling pageviews on behalf of publishers to advertisers. In such settings, revenue sharing is a central part of running such a marketplace for the intermediary, and fixed-percentage revenue sharing schemes are often used to split the revenue among the platform and the sellers. In particular, such revenue sharing schemes require the platform to (i) take at most a constant fraction \alpha of the revenue from auctions and (ii) pay the seller at least the seller declared opportunity cost c for each item sold. A straightforward way to satisfy the constraints is to set a reserve price at c / (1 - \alpha) for each item, but it is not the optimal solution on maximizing the profit of the intermediary. While previous studies (by Mirrokni and Gomes, and by Niazadeh et al) focused on revenue-sharing schemes in static double auctions, in this paper, we take advantage of the repeated nature of the auctions. In particular, we introduce dynamic revenue sharing schemes where we balance the two constraints over different auctions to achieve higher profit and seller revenue. This is directly motivated by the practice of advertising exchanges where the fixed-percentage revenue-share should be met across all auctions and not in each auction. In this paper, we characterize the optimal revenue sharing scheme that satisfies both constraints in expectation. Finally, we empirically evaluate our revenue sharing scheme on real data.
Author Information
Santiago Balseiro (Duke University)
Max Lin (Google)
Vahab Mirrokni (Google Research NYC)
Renato Leme (Google Research)
IIIS Song Zuo (IIIS, Tsinghua University)
More from the Same Authors
-
2021 Poster: Contextual Recommendations and Low-Regret Cutting-Plane Algorithms »
Sreenivas Gollapudi · Guru Guruganesh · Kostas Kollias · Pasin Manurangsi · Renato Leme · Jon Schneider -
2020 Poster: Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions »
Alessandro Epasto · Mohammad Mahdian · Vahab Mirrokni · Emmanouil Zampetakis -
2020 Spotlight: Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions »
Alessandro Epasto · Mohammad Mahdian · Vahab Mirrokni · Emmanouil Zampetakis -
2020 Poster: Smoothly Bounding User Contributions in Differential Privacy »
Alessandro Epasto · Mohammad Mahdian · Jieming Mao · Vahab Mirrokni · Lijie Ren -
2020 Poster: Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming »
Joey Huchette · Haihao Lu · Hossein Esfandiari · Vahab Mirrokni -
2020 Poster: Myersonian Regression »
Allen Liu · Renato Leme · Jon Schneider -
2020 : Clustering At Scale »
Vahab Mirrokni -
2020 Expo Workshop: Mining and Learning with Graphs at Scale »
Vahab Mirrokni · Bryan Perozzi · Jakub Lacki · Jonathan Halcrow · Jaqui C Herman -
2020 : Introduction »
Vahab Mirrokni -
2019 Poster: Contextual Bandits with Cross-Learning »
Santiago Balseiro · Negin Golrezaei · Mohammad Mahdian · Vahab Mirrokni · Jon Schneider -
2019 Poster: Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions »
Negin Golrezaei · Adel Javanmard · Vahab Mirrokni -
2019 Poster: Secretary Ranking with Minimal Inversions »
Sepehr Assadi · Eric Balkanski · Renato Leme -
2019 Poster: A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions »
Yuan Deng · Sébastien Lahaie · Vahab Mirrokni -
2019 Poster: Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond »
Lin Chen · Hossein Esfandiari · Gang Fu · Vahab Mirrokni -
2019 Poster: Variance Reduction in Bipartite Experiments through Correlation Clustering »
Jean Pouget-Abadie · Kevin Aydin · Warren Schudy · Kay Brodersen · Vahab Mirrokni -
2018 Poster: Contextual Pricing for Lipschitz Buyers »
Jieming Mao · Renato Leme · Jon Schneider -
2017 Poster: Affinity Clustering: Hierarchical Clustering at Scale »
Mohammadhossein Bateni · Soheil Behnezhad · Mahsa Derakhshan · MohammadTaghi Hajiaghayi · Raimondas Kiveris · Silvio Lattanzi · Vahab Mirrokni -
2016 Poster: Bi-Objective Online Matching and Submodular Allocations »
Hossein Esfandiari · Nitish Korula · Vahab Mirrokni -
2016 Poster: Linear Relaxations for Finding Diverse Elements in Metric Spaces »
Aditya Bhaskara · Mehrdad Ghadiri · Vahab Mirrokni · Ola Svensson -
2014 Poster: Distributed Balanced Clustering via Mapping Coresets »
Mohammadhossein Bateni · Aditya Bhaskara · Silvio Lattanzi · Vahab Mirrokni