341 research outputs found
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
Reserve price optimization in display advertising
Display advertising is the main type of online advertising, and it comes in the form of banner ads and rich media on publishers\u27 websites. Publishers sell ad impressions, where an impression is one display of an ad in a web page. A common way to sell ad impressions is through real-time bidding (RTB). In 2019, advertisers in the United States spent nearly 60 billion U.S. dollars on programmatic digital display advertising. By 2022, expenditures are expected to increase to nearly 95 billion U.S. dollars. In general, the remaining impressions are sold directly by the publishers. The only way for publishers to control the price of the impressions they sell through RTB is by setting up a reserve price, which has to be beaten by the winning bids.
The two main types of RTB auction strategies are 1) first-price auctions, i.e., the winning advertiser pays the highest bid, and 2) second-price auctions, i.e., the winning advertiser pays the maximum of the second highest bid and the reserve price (the minimum price that a publisher can accept for an impression). In both types of auctions, bids lower than the reserve prices will be automatically rejected. Since both strategies are influenced by the reserve price, setting a good reserve price is an important, but challenging task for publishers. A high reserve price may lead to very few winning bids, and thus can decrease the revenue substantially. A low reserve price may devalue the impressions and hurt the revenue because advertisers do not need to bid high to beat the reserve. Reduction of ad revenue may affect the quality of free content and publishers\u27 business sustainability. Therefore, in an ideal situation, the publishers would like to set the reserve price as high as possible, while ensuring that there is a winning bid.
This dissertation proposes to use machine learning techniques to determine the optimal reserve prices for individual impressions in real-time, with the goal of maximizing publishers\u27 ad revenue. The proposed techniques are practical because they use data only available to publishers. They are also general because they can be applied to most online publishers. The novelty of the research comes from both the problem, which was not studied before, and the proposed techniques, which are adapted to the online publishing domain.
For second-price auctions, a survival-analysis-based model is first proposed to predict failure rates of reserve prices of specific impressions in second-price auctions. It uses factorization machines (FM) to capture feature interaction and header bidding information to improve the prediction performance. The experiments, using data from a large media company, show that the proposed model for failure rate prediction outperforms the comparative systems. The survival-analysis-based model is augmented further with a deep neural network (DNN) to capture the feature interaction. The experiments show that the DNN-based model further improves the performance from the FM-based one.
For first-price auctions, a multi-task learning framework is proposed to predict the lower bounds of highest bids with a coverage probability. The model can guarantee the highest bids of at least a certain percentage of impressions are more than the corresponding predicted lower bounds. Setting the final reserve prices to the lower bounds, the model can guarantee a certain percentage of outbid impressions in real-time bidding. The experiments show that the proposed method can significantly outperform the comparison systems
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
Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising
Real-time bidding (RTB) based display advertising has become one of the key
technological advances in computational advertising. RTB enables advertisers to
buy individual ad impressions via an auction in real-time and facilitates the
evaluation and the bidding of individual impressions across multiple
advertisers. In RTB, the advertisers face three main challenges when optimizing
their bidding strategies, namely (i) estimating the utility (e.g., conversions,
clicks) of the ad impression, (ii) forecasting the market value (thus the cost)
of the given ad impression, and (iii) deciding the optimal bid for the given
auction based on the first two. Previous solutions assume the first two are
solved before addressing the bid optimization problem. However, these
challenges are strongly correlated and dealing with any individual problem
independently may not be globally optimal. In this paper, we propose Bidding
Machine, a comprehensive learning to bid framework, which consists of three
optimizers dealing with each challenge above, and as a whole, jointly optimizes
these three parts. We show that such a joint optimization would largely
increase the campaign effectiveness and the profit. From the learning
perspective, we show that the bidding machine can be updated smoothly with both
offline periodical batch or online sequential training schemes. Our extensive
offline empirical study and online A/B testing verify the high effectiveness of
the proposed bidding machine.Comment: 18 pages, 10 figures, Final version published in IEEE Transactions on
Knowledge and Data Engineering (TKDE), URL:
http://ieeexplore.ieee.org/document/8115218
Bid Shading by Win-Rate Estimation and Surplus Maximization
This paper describes a new win-rate based bid shading algorithm (WR) that
does not rely on the minimum-bid-to-win feedback from a Sell-Side Platform
(SSP). The method uses a modified logistic regression to predict the profit
from each possible shaded bid price. The function form allows fast maximization
at run-time, a key requirement for Real-Time Bidding (RTB) systems. We report
production results from this method along with several other algorithms. We
found that bid shading, in general, can deliver significant value to
advertisers, reducing price per impression to about 55% of the unshaded cost.
Further, the particular approach described in this paper captures 7% more
profit for advertisers, than do benchmark methods of just bidding the most
probable winning price. We also report 4.3% higher surplus than an industry
Sell-Side Platform shading service. Furthermore, we observed 3% - 7% lower
eCPM, eCPC and eCPA when the algorithm was integrated with budget controllers.
We attribute the gains above as being mainly due to the explicit maximization
of the surplus function, and note that other algorithms can take advantage of
this same approach.Comment: AdKDD 202
Improving the Use of Experimental Auctions in Africa: Theory and Evidence
Experimental auctions have not been widely used in Africa. However, auctions are important tools for evaluating new products and technologies. To increase the quality of these experiments, we explore an alternative first-price bidding mechanism that is more similar to African market exchanges and we analyze factors likely to affect bidding. Experiments with African consumers show that the proposed first-price mechanism has no advantage over conventional second-price mechanisms. Results show high and significant cash-in-hand, experimenter, and time of day effects in main rounds, and significant ordering effects in test rounds. These effects need to be carefully considered when applying the Becker-DeGroot-Marschak mechanism in Africa.Africa, BDM mechanism, experimenter effect, first-price auction, income effect, order effect, time of day effect, Research Methods/ Statistical Methods,
Real-Time Optimization Of Web Publisher RTB Revenues
This paper describes an engine to optimize web publisher revenues from
second-price auctions. These auctions are widely used to sell online ad spaces
in a mechanism called real-time bidding (RTB). Optimization within these
auctions is crucial for web publishers, because setting appropriate reserve
prices can significantly increase revenue. We consider a practical real-world
setting where the only available information before an auction occurs consists
of a user identifier and an ad placement identifier. The real-world challenges
we had to tackle consist mainly of tracking the dependencies on both the user
and placement in an highly non-stationary environment and of dealing with
censored bid observations. These challenges led us to make the following design
choices: (i) we adopted a relatively simple non-parametric regression model of
auction revenue based on an incremental time-weighted matrix factorization
which implicitly builds adaptive users' and placements' profiles; (ii) we
jointly used a non-parametric model to estimate the first and second bids'
distribution when they are censored, based on an on-line extension of the
Aalen's Additive model.
Our engine is a component of a deployed system handling hundreds of web
publishers across the world, serving billions of ads a day to hundreds of
millions of visitors. The engine is able to predict, for each auction, an
optimal reserve price in approximately one millisecond and yields a significant
revenue increase for the web publishers
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
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection
Optimized Cost per Click in Taobao Display Advertising
Taobao, as the largest online retail platform in the world, provides billions
of online display advertising impressions for millions of advertisers every
day. For commercial purposes, the advertisers bid for specific spots and target
crowds to compete for business traffic. The platform chooses the most suitable
ads to display in tens of milliseconds. Common pricing methods include cost per
mille (CPM) and cost per click (CPC). Traditional advertising systems target
certain traits of users and ad placements with fixed bids, essentially regarded
as coarse-grained matching of bid and traffic quality. However, the fixed bids
set by the advertisers competing for different quality requests cannot fully
optimize the advertisers' key requirements. Moreover, the platform has to be
responsible for the business revenue and user experience. Thus, we proposed a
bid optimizing strategy called optimized cost per click (OCPC) which
automatically adjusts the bid to achieve finer matching of bid and traffic
quality of page view (PV) request granularity. Our approach optimizes
advertisers' demands, platform business revenue and user experience and as a
whole improves traffic allocation efficiency. We have validated our approach in
Taobao display advertising system in production. The online A/B test shows our
algorithm yields substantially better results than previous fixed bid manner.Comment: Accepted by KDD 201
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