21,661 research outputs found
Real-Time Bidding by Reinforcement Learning in Display Advertising
The majority of online display ads are served through real-time bidding (RTB)
--- each ad display impression is auctioned off in real-time when it is just
being generated from a user visit. To place an ad automatically and optimally,
it is critical for advertisers to devise a learning algorithm to cleverly bid
an ad impression in real-time. Most previous works consider the bid decision as
a static optimization problem of either treating the value of each impression
independently or setting a bid price to each segment of ad volume. However, the
bidding for a given ad campaign would repeatedly happen during its life span
before the budget runs out. As such, each bid is strategically correlated by
the constrained budget and the overall effectiveness of the campaign (e.g., the
rewards from generated clicks), which is only observed after the campaign has
completed. Thus, it is of great interest to devise an optimal bidding strategy
sequentially so that the campaign budget can be dynamically allocated across
all the available impressions on the basis of both the immediate and future
rewards. In this paper, we formulate the bid decision process as a
reinforcement learning problem, where the state space is represented by the
auction information and the campaign's real-time parameters, while an action is
the bid price to set. By modeling the state transition via auction competition,
we build a Markov Decision Process framework for learning the optimal bidding
policy to optimize the advertising performance in the dynamic real-time bidding
environment. Furthermore, the scalability problem from the large real-world
auction volume and campaign budget is well handled by state value approximation
using neural networks.Comment: WSDM 201
Managing Risk of Bidding in Display Advertising
In this paper, we deal with the uncertainty of bidding for display
advertising. Similar to the financial market trading, real-time bidding (RTB)
based display advertising employs an auction mechanism to automate the
impression level media buying; and running a campaign is no different than an
investment of acquiring new customers in return for obtaining additional
converted sales. Thus, how to optimally bid on an ad impression to drive the
profit and return-on-investment becomes essential. However, the large
randomness of the user behaviors and the cost uncertainty caused by the auction
competition may result in a significant risk from the campaign performance
estimation. In this paper, we explicitly model the uncertainty of user
click-through rate estimation and auction competition to capture the risk. We
borrow an idea from finance and derive the value at risk for each ad display
opportunity. Our formulation results in two risk-aware bidding strategies that
penalize risky ad impressions and focus more on the ones with higher expected
return and lower risk. The empirical study on real-world data demonstrates the
effectiveness of our proposed risk-aware bidding strategies: yielding profit
gains of 15.4% in offline experiments and up to 17.5% in an online A/B test on
a commercial RTB platform over the widely applied bidding strategies
PhotoRaptor - Photometric Research Application To Redshifts
Due to the necessity to evaluate photo-z for a variety of huge sky survey
data sets, it seemed important to provide the astronomical community with an
instrument able to fill this gap. Besides the problem of moving massive data
sets over the network, another critical point is that a great part of
astronomical data is stored in private archives that are not fully accessible
on line. So, in order to evaluate photo-z it is needed a desktop application
that can be downloaded and used by everyone locally, i.e. on his own personal
computer or more in general within the local intranet hosted by a data center.
The name chosen for the application is PhotoRApToR, i.e. Photometric Research
Application To Redshift (Cavuoti et al. 2015, 2014; Brescia 2014b). It embeds a
machine learning algorithm and special tools dedicated to preand
post-processing data. The ML model is the MLPQNA (Multi Layer Perceptron
trained by the Quasi Newton Algorithm), which has been revealed particularly
powerful for the photo-z calculation on the base of a spectroscopic sample
(Cavuoti et al. 2012; Brescia et al. 2013, 2014a; Biviano et al. 2013).
The PhotoRApToR program package is available, for different platforms, at the
official website (http://dame.dsf.unina.it/dame_photoz.html#photoraptor).Comment: User Manual of the PhotoRaptor tool, 54 pages. arXiv admin note:
substantial text overlap with arXiv:1501.0650
Improving Estimation in Functional Linear Regression with Points of Impact: Insights into Google AdWords
The functional linear regression model with points of impact is a recent
augmentation of the classical functional linear model with many practically
important applications. In this work, however, we demonstrate that the existing
data-driven procedure for estimating the parameters of this regression model
can be very instable and inaccurate. The tendency to omit relevant points of
impact is a particularly problematic aspect resulting in omitted-variable
biases. We explain the theoretical reason for this problem and propose a new
sequential estimation algorithm that leads to significantly improved estimation
results. Our estimation algorithm is compared with the existing estimation
procedure using an in-depth simulation study. The applicability is demonstrated
using data from Google AdWords, today's most important platform for online
advertisements. The \textsf{R}-package \texttt{FunRegPoI} and additional
\textsf{R}-codes are provided in the online supplementary material
Brain-mediated Transfer Learning of Convolutional Neural Networks
The human brain can effectively learn a new task from a small number of
samples, which indicate that the brain can transfer its prior knowledge to
solve tasks in different domains. This function is analogous to transfer
learning (TL) in the field of machine learning. TL uses a well-trained feature
space in a specific task domain to improve performance in new tasks with
insufficient training data. TL with rich feature representations, such as
features of convolutional neural networks (CNNs), shows high generalization
ability across different task domains. However, such TL is still insufficient
in making machine learning attain generalization ability comparable to that of
the human brain. To examine if the internal representation of the brain could
be used to achieve more efficient TL, we introduce a method for TL mediated by
human brains. Our method transforms feature representations of audiovisual
inputs in CNNs into those in activation patterns of individual brains via their
association learned ahead using measured brain responses. Then, to estimate
labels reflecting human cognition and behavior induced by the audiovisual
inputs, the transformed representations are used for TL. We demonstrate that
our brain-mediated TL (BTL) shows higher performance in the label estimation
than the standard TL. In addition, we illustrate that the estimations mediated
by different brains vary from brain to brain, and the variability reflects the
individual variability in perception. Thus, our BTL provides a framework to
improve the generalization ability of machine-learning feature representations
and enable machine learning to estimate human-like cognition and behavior,
including individual variability
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A person’s
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the user’s experience than generic
parameters, accurate predictions reveal important aspects of user’s
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumer’s buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five users’
personality factors and 1,200 personal users’ pictures
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