3,182 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
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
Deep Recurrent Survival Analysis
Survival analysis is a hotspot in statistical research for modeling
time-to-event information with data censorship handling, which has been widely
used in many applications such as clinical research, information system and
other fields with survivorship bias. Many works have been proposed for survival
analysis ranging from traditional statistic methods to machine learning models.
However, the existing methodologies either utilize counting-based statistics on
the segmented data, or have a pre-assumption on the event probability
distribution w.r.t. time. Moreover, few works consider sequential patterns
within the feature space. In this paper, we propose a Deep Recurrent Survival
Analysis model which combines deep learning for conditional probability
prediction at fine-grained level of the data, and survival analysis for
tackling the censorship. By capturing the time dependency through modeling the
conditional probability of the event for each sample, our method predicts the
likelihood of the true event occurrence and estimates the survival rate over
time, i.e., the probability of the non-occurrence of the event, for the
censored data. Meanwhile, without assuming any specific form of the event
probability distribution, our model shows great advantages over the previous
works on fitting various sophisticated data distributions. In the experiments
on the three real-world tasks from different fields, our model significantly
outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code:
https://github.com/rk2900/drs
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
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
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
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
Forecasting online advertising costs: interpretable deep learning of the competitive bidding landscape
As advertisers increasingly shift their budgets toward digital advertising, forecasting ad vertising costs is essential for making budget plans to optimize marketing campaign re turns. In this paper, we perform a comprehensive study using a variety of time-series
forecasting methods to predict daily average cost-per-click in the online advertising mar ket. We show that forecasting advertising costs would benefit from multivariate models
using covariates from competitors’ cost-per-click development identified through time series clustering. We further interpret the results by analyzing feature importance and
temporal attention. Finally, we show that our approach holds several advantages over,
first, models that individual advertisers might build based on their own data, and second,
existing tools from Google
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