@inproceedings{9908d18e469749c885edd36595bd48d3,
title = "Reserve Price optimization in First-Price Auctions via Multi-Task Learning",
abstract = "Online publishers typically sell ad impressions through auctions held in ad exchanges in real-time, i.e., real-time bidding (RTB). A publisher will accept the winning bid if it is higher than a given reserve price for an ad impression. Setting an appropriate reserve price for an ad impression is critical for publishers' revenue generation, but also challenging. While this problem has been studied for second-price auctions, it lacks studies for first-price auctions, the de facto industry standard since 2019. This paper proposes a machine learning model that determines the optimal reserve prices for individual ad impressions in real-time. It uses a multi-task learning framework to predict the lower bounds of the highest bids with a coverage probability, using only the data available to publishers. The experiments using data from a large international publisher show that the proposed model outperforms the comparison systems on generating revenue.",
keywords = "computational advertising, neural networks, prediction interval estimation, proportional hazards model, survival analysis",
author = "Achir Kalra and Chong Wang and Cristian Borcea and Yi Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 23rd IEEE International Conference on Data Mining, ICDM 2023 ; Conference date: 01-12-2023 Through 04-12-2023",
year = "2023",
doi = "10.1109/ICDM58522.2023.00029",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "200--209",
editor = "Guihai Chen and Latifur Khan and Xiaofeng Gao and Meikang Qiu and Witold Pedrycz and Xindong Wu",
booktitle = "Proceedings - 23rd IEEE International Conference on Data Mining, ICDM 2023",
address = "United States",
}