Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/83953
Type: | Conference paper |
Title: | Revenue maximization via hiding item attributes |
Author: | Guo, M. Deligkas, A. |
Citation: | Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 13), 2013 / F. Rossi (ed.): pp.157-163 |
Publisher: | AAAI Press |
Publisher Place: | California; USA |
Issue Date: | 2013 |
ISBN: | 9781577356332 |
ISSN: | 1045-0823 |
Conference Name: | International Joint Conference on Artificial Intelligence (23rd : 2014 : Beijing, China) |
Statement of Responsibility: | Mingyu Guo, Argyrios Deligkas |
Abstract: | We study probabilistic single-item second-price auctions where the item is characterized by a set of attributes. The auctioneer knows the actual instantiation of all the attributes, but he may choose to reveal only a subset of these attributes to the bidders. Our model is an abstraction of the following Ad auction scenario. The website (auctioneer) knows the demographic information of its impressions, and this information is in terms of a list of attributes (e.g., age, gender, country of location). The website may hide certain attributes from its advertisers (bidders) in order to create thicker market, which may lead to higher revenue. We study how to hide attributes in an optimal way. We show that it is NP-hard to compute the optimal attribute hiding scheme. We then derive a polynomial-time solvable upper bound on the optimal revenue. Finally, we propose two heuristicbased attribute hiding schemes. Experiments show that revenue achieved by these schemes is close to the upper bound. |
Rights: | Copyright © 2013 International Joint Conferences on Artificial Intelligence |
Description (link): | http://ijcai.org/papers13/contents.php |
Published version: | http://ijcai.org/papers13/Papers/IJCAI13-033.pdf |
Appears in Collections: | Aurora harvest Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
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RA_hdl_83953.pdf | Restricted Access | 544.6 kB | Adobe PDF | View/Open |
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