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A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations.
David
Pardoe and Peter Stone.
In Proceedings of the 10th International
Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2011.
[PDF]313.6kB [postscript]280.8kB
Keyword auctions are becoming increasingly important in today's electronic marketplaces. One of their most challenging aspects is the limited amount of information revealed about other advertisers. In this paper, we present a particle filter that can be used to estimate the bids of other advertisers given a periodic ranking of their bids. This particle filter makes use of models of the bidding behavior of other advertisers, and so we also show how such models can be learned from past bidding data. In experiments in the Ad Auction scenario of the Trading Agent Competition, the combination of this particle filter and bidder modeling outperforms all other bid estimation methods tested.
@InProceedings{AAMAS11-pardoe,
author = {David Pardoe and Peter Stone},
title = {A Particle Filter for Bid Estimation in Ad Auctions with Periodic Ranking Observations},
booktitle = {Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
location = {Taipei, Taiwan},
month = {May},
year = {2011},
abstract = {
Keyword auctions are becoming increasingly important in
today's electronic marketplaces. One of their most challenging
aspects is the limited amount of information revealed
about other advertisers. In this paper, we present a particle
filter that can be used to estimate the bids of other advertisers
given a periodic ranking of their bids. This particle filter
makes use of models of the bidding behavior of other advertisers,
and so we also show how such models can be learned
from past bidding data. In experiments in the Ad Auction
scenario of the Trading Agent Competition, the combination
of this particle filter and bidder modeling outperforms
all other bid estimation methods tested.
},
}
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