Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)

Luca D'Amico-Wong, Gary Qiurui Ma, David C. Parkes

AAAI 2024 pp. 23468-23470

doi:10.1609/AAAI.V38I21.30432 /aaai/2024/daposamicowong2024aaai-strategic/

Abstract

We consider a platform in a two-sided market with unit-supply sellers and unit-demand buyers. Each buyer can transact with a subset of sellers it knows off platform and another seller that the platform recommends. Given the choice of sellers, transactions and prices form a competitive equilibrium. The platform selects one seller for each buyer, and charges a fixed percentage of prices to all transactions that it recommends. The platform seeks to maximize total revenue. We show that the platform's problem is NP-hard, even when each buyer knows at most two buyers off platform. Finally, when each buyer values all sellers equally and knows only one buyer off platform, we provide a polynomial time algorithm that optimally solves the problem.

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Cite

Text

D'Amico-Wong et al. "Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30432

Markdown

[D'Amico-Wong et al. "Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/daposamicowong2024aaai-strategic/) doi:10.1609/AAAI.V38I21.30432

BibTeX

@inproceedings{daposamicowong2024aaai-strategic,
  title     = {{Strategic Recommendation: Revenue Optimal Matching for Online Platforms (Student Abstract)}},
  author    = {D'Amico-Wong, Luca and Ma, Gary Qiurui and Parkes, David C.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2024},
  pages     = {23468-23470},
  doi       = {10.1609/AAAI.V38I21.30432},
  url       = {https://mlanthology.org/aaai/2024/daposamicowong2024aaai-strategic/}
}