1,035 research outputs found
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search
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 optimize 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 \emph{game-theoretic machine
learning} approach, which naturally combines machine learning and game theory,
and learns the auction mechanism using a bilevel optimization 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 maximization 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 optimize this empirical
revenue. Our experiments indicate that the proposed approach is able to produce
a much more effective auction mechanism than several baselines.Comment: Twenty-third International Conference on Artificial Intelligence
(IJCAI 2013
Agent Behavior Prediction and Its Generalization Analysis
Machine learning algorithms have been applied to predict agent behaviors in
real-world dynamic systems, such as advertiser behaviors in sponsored search
and worker behaviors in crowdsourcing. The behavior data in these systems are
generated by live agents: once the systems change due to the adoption of the
prediction models learnt from the behavior data, agents will observe and
respond to these changes by changing their own behaviors accordingly. As a
result, the behavior data will evolve and will not be identically and
independently distributed, posing great challenges to the theoretical analysis
on the machine learning algorithms for behavior prediction. To tackle this
challenge, in this paper, we propose to use Markov Chain in Random Environments
(MCRE) to describe the behavior data, and perform generalization analysis of
the machine learning algorithms on its basis. Since the one-step transition
probability matrix of MCRE depends on both previous states and the random
environment, conventional techniques for generalization analysis cannot be
directly applied. To address this issue, we propose a novel technique that
transforms the original MCRE into a higher-dimensional time-homogeneous Markov
chain. The new Markov chain involves more variables but is more regular, and
thus easier to deal with. We prove the convergence of the new Markov chain when
time approaches infinity. Then we prove a generalization bound for the machine
learning algorithms on the behavior data generated by the new Markov chain,
which depends on both the Markovian parameters and the covering number of the
function class compounded by the loss function for behavior prediction and the
behavior prediction model. To the best of our knowledge, this is the first work
that performs the generalization analysis on data generated by complex
processes in real-world dynamic systems
07271 Abstracts Collection -- Computational Social Systems and the Internet
From 01.07. to 06.07.2007, the Dagstuhl Seminar 07271 ``Computational Social Systems and the Internet\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
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