Learning Optimal Auction Mechanism in Sponsored Search

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{He:2013:IJCAI,
  author =       "Di He and Wei Chen and Liwei Wang and Tie-Yan Liu",
  title =        "Learning Optimal Auction Mechanism in Sponsored
                 Search",
  booktitle =    "Twenty-third International Conference on Artificial
                 Intelligence, IJCAI 2013",
  year =         "2013",
  editor =       "Francesca Rossi",
  address =      "Beijing, China",
  month =        aug # " 3-9",
  keywords =     "genetic algorithms, genetic programming, computer
                 science - computer science and game theory, computer
                 science - learning",
  URL =          "http://arxiv.org/abs/1406.0728",
  abstract =     "Sponsored search is an important monetization channel
                 for search engines, in which an auction mechanism is
                 used to select the ads shown to users and determine the
                 prices charged from advertisers. There have been
                 several pieces of work in the literature that
                 investigate how to design an auction mechanism in order
                 to optimise the revenue of the search engine. However,
                 due to some unrealistic assumptions used, the practical
                 values of these studies are not very clear. In this
                 paper, we propose a novel game-theoretic machine
                 learning approach, which naturally combines machine
                 learning and game theory, and learns the auction
                 mechanism using a bilevel optimisation framework. In
                 particular, we first learn a Markov model from
                 historical data to describe how advertisers change
                 their bids in response to an auction mechanism, and
                 then for any given auction mechanism, we use the learnt
                 model to predict its corresponding future bid
                 sequences. Next we learn the auction mechanism through
                 empirical revenue maximisation on the predicted bid
                 sequences. We show that the empirical revenue will
                 converge when the prediction period approaches
                 infinity, and a Genetic Programming algorithm can
                 effectively optimise this empirical revenue. Our
                 experiments indicate that the proposed approach is able
                 to produce a much more effective auction mechanism than
                 several baselines.

                 abstract from oai:arXiv.org:1406.0728",
  notes =        "see also A Game-theoretic Machine Learning Approach
                 for Revenue Maximization in Sponsored Search
                 \cite{oai:arXiv.org:1406.0728}
                 http://ijcai13.org/program/accepted_papers",
}

Genetic Programming entries for Di He Wei Chen Liwei Wang Tie-yan Liu

Citations