9 research outputs found
Bid Optimization by Multivariable Control in Display Advertising
Real-Time Bidding (RTB) is an important paradigm in display advertising,
where advertisers utilize extended information and algorithms served by Demand
Side Platforms (DSPs) to improve advertising performance. A common problem for
DSPs is to help advertisers gain as much value as possible with budget
constraints. However, advertisers would routinely add certain key performance
indicator (KPI) constraints that the advertising campaign must meet due to
practical reasons. In this paper, we study the common case where advertisers
aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a
KPI constraint. We convert such a problem into a linear programming problem and
leverage the primal-dual method to derive the optimal bidding strategy. To
address the applicability issue, we propose a feedback control-based solution
and devise the multivariable control system. The empirical study based on
real-word data from Taobao.com verifies the effectiveness and superiority of
our approach compared with the state of the art in the industry practices
Deep Landscape Forecasting for Real-time Bidding Advertising
The emergence of real-time auction in online advertising has drawn huge
attention of modeling the market competition, i.e., bid landscape forecasting.
The problem is formulated as to forecast the probability distribution of market
price for each ad auction. With the consideration of the censorship issue which
is caused by the second-price auction mechanism, many researchers have devoted
their efforts on bid landscape forecasting by incorporating survival analysis
from medical research field. However, most existing solutions mainly focus on
either counting-based statistics of the segmented sample clusters, or learning
a parameterized model based on some heuristic assumptions of distribution
forms. Moreover, they neither consider the sequential patterns of the feature
over the price space. In order to capture more sophisticated yet flexible
patterns at fine-grained level of the data, we propose a Deep Landscape
Forecasting (DLF) model which combines deep learning for probability
distribution forecasting and survival analysis for censorship handling.
Specifically, we utilize a recurrent neural network to flexibly model the
conditional winning probability w.r.t. each bid price. Then we conduct the bid
landscape forecasting through probability chain rule with strict mathematical
derivations. And, in an end-to-end manner, we optimize the model by minimizing
two negative likelihood losses with comprehensive motivations. Without any
specific assumption for the distribution form of bid landscape, our model shows
great advantages over previous works on fitting various sophisticated market
price distributions. In the experiments over two large-scale real-world
datasets, our model significantly outperforms the state-of-the-art solutions
under various metrics.Comment: KDD 2019. The reproducible code and dataset link is
https://github.com/rk2900/DL
Generalized Multi-Output Gaussian Process Censored Regression
When modelling censored observations, a typical approach in current
regression methods is to use a censored-Gaussian (i.e. Tobit) model to describe
the conditional output distribution. In this paper, as in the case of missing
data, we argue that exploiting correlations between multiple outputs can enable
models to better address the bias introduced by censored data. To do so, we
introduce a heteroscedastic multi-output Gaussian process model which combines
the non-parametric flexibility of GPs with the ability to leverage information
from correlated outputs under input-dependent noise conditions. To address the
resulting inference intractability, we further devise a variational bound to
the marginal log-likelihood suitable for stochastic optimization. We
empirically evaluate our model against other generative models for censored
data on both synthetic and real world tasks and further show how it can be
generalized to deal with arbitrary likelihood functions. Results show how the
added flexibility allows our model to better estimate the underlying
non-censored (i.e. true) process under potentially complex censoring dynamics.Comment: 7 pages, 3 figures, 3 table
Towards a User Privacy-Aware Mobile Gaming App Installation Prediction Model
Over the past decade, programmatic advertising has received a great deal of
attention in the online advertising industry. A real-time bidding (RTB) system
is rapidly becoming the most popular method to buy and sell online advertising
impressions. Within the RTB system, demand-side platforms (DSP) aim to spend
advertisers' campaign budgets efficiently while maximizing profit, seeking
impressions that result in high user responses, such as clicks or installs. In
the current study, we investigate the process of predicting a mobile gaming app
installation from the point of view of a particular DSP, while paying attention
to user privacy, and exploring the trade-off between privacy preservation and
model performance. There are multiple levels of potential threats to user
privacy, depending on the privacy leaks associated with the data-sharing
process, such as data transformation or de-anonymization. To address these
concerns, privacy-preserving techniques were proposed, such as cryptographic
approaches, for training privacy-aware machine-learning models. However, the
ability to train a mobile gaming app installation prediction model without
using user-level data, can prevent these threats and protect the users'
privacy, even though the model's ability to predict may be impaired.
Additionally, current laws might force companies to declare that they are
collecting data, and might even give the user the option to opt out of such
data collection, which might threaten companies' business models in digital
advertising, which are dependent on the collection and use of user-level data.
We conclude that privacy-aware models might still preserve significant
capabilities, enabling companies to make better decisions, dependent on the
privacy-efficacy trade-off utility function of each case.Comment: 11 pages, 3 figure
Bid Shading in The Brave New World of First-Price Auctions
Online auctions play a central role in online advertising, and are one of the
main reasons for the industry's scalability and growth. With great changes in
how auctions are being organized, such as changing the second- to first-price
auction type, advertisers and demand platforms are compelled to adapt to a new
volatile environment. Bid shading is a known technique for preventing
overpaying in auction systems that can help maintain the strategy equilibrium
in first-price auctions, tackling one of its greatest drawbacks. In this study,
we propose a machine learning approach of modeling optimal bid shading for
non-censored online first-price ad auctions. We clearly motivate the approach
and extensively evaluate it in both offline and online settings on a major
demand side platform. The results demonstrate the superiority and robustness of
the new approach as compared to the existing approaches across a range of
performance metrics.Comment: In Proceedings of the 29th ACM International Conference on
Information and Knowledge Management (CIKM'20), October 19-23, 2020, Virtual
Event, Irelan
Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes
Transport demand is highly dependent on supply, especially for shared
transport services where availability is often limited. As observed demand
cannot be higher than available supply, historical transport data typically
represents a biased, or censored, version of the true underlying demand
pattern. Without explicitly accounting for this inherent distinction,
predictive models of demand would necessarily represent a biased version of
true demand, thus less effectively predicting the needs of service users. To
counter this problem, we propose a general method for censorship-aware demand
modeling, for which we devise a censored likelihood function. We apply this
method to the task of shared mobility demand prediction by incorporating the
censored likelihood within a Gaussian Process model, which can flexibly
approximate arbitrary functional forms. Experiments on artificial and
real-world datasets show how taking into account the limiting effect of supply
on demand is essential in the process of obtaining an unbiased predictive model
of user demand behavior.Comment: 21 pages, 10 figure