50 research outputs found
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
Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models
In this work, we introduce the notion of Context-Based Prediction Models. A
Context-Based Prediction Model determines the probability of a user's action
(such as a click or a conversion) solely by relying on user and contextual
features, without considering any specific features of the item itself. We have
identified numerous valuable applications for this modeling approach, including
training an auxiliary context-based model to estimate click probability and
incorporating its prediction as a feature in CTR prediction models. Our
experiments indicate that this enhancement brings significant improvements in
offline and online business metrics while having minimal impact on the cost of
serving. Overall, our work offers a simple and scalable, yet powerful approach
for enhancing the performance of large-scale commercial recommender systems,
with broad implications for the field of personalized recommendations
Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding
In this work, we present a scalable and efficient system for exploring the
supply landscape in real-time bidding. The system directs exploration based on
the predictive uncertainty of models used for click-through rate prediction and
works in a high-throughput, low-latency environment. Through online A/B
testing, we demonstrate that exploration with model uncertainty has a positive
impact on model performance and business KPIs
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Identity, advertising, and algorithmic targeting: or how (not) to target your “ideal user”
Targeted or “personalized” marketing is an everyday part of most web users’ experience. But how do companies “personalize” commercial web content in the context of mass data aggregation? What does it really mean to use data to target web users by their “personal interests” and individual identities? What kinds of ethical implications arise from such practices? This case study explores commercial algorithmic profiling, targeting, and advertising systems, considering the extent to which such systems can be ethical. To do so the case study first maps a brief history of the commercially targeted user, then explores how web users themselves perceive targeted advertising in relation to data knowledge, cookie consent, and “algorithmic disillusionment.” It goes on to analyze current regulatory landscapes and consider how developers who target audiences might avoid placing burdens of impossible data choice on web users themselves. Finally, it offers a series of reflections on best practice in terms of how (not) to profile and target web users. To illuminate the ethical considerations connected to commercial targeted advertising systems, this case study presents some study tasks (see Exercises 1 and 2) that can be used as discussion points for those interested in exploring the nuances of targeting in specific contexts
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
DCRMTA: Unbiased Causal Representation for Multi-touch Attribution
Multi-touch attribution (MTA) currently plays a pivotal role in achieving a
fair estimation of the contributions of each advertising touchpoint to-wards
conversion behavior, deeply influencing budget allocation and advertising
recommenda-tion. Previous works attempted to eliminate the bias caused by user
preferences to achieve the unbiased assumption of the conversion model. The
multi-model collaboration method is not ef-ficient, and the complete
elimination of user in-fluence also eliminates the causal effect of user
features on conversion, resulting in limited per-formance of the conversion
model. This paper re-defines the causal effect of user features on con-versions
and proposes a novel end-to-end ap-proach, Deep Causal Representation for MTA
(DCRMTA). Our model focuses on extracting causa features between conversions
and users while eliminating confounding variables. Fur-thermore, extensive
experiments demonstrate DCRMTA's superior performance in converting prediction
across varying data distributions, while also effectively attributing value
across dif-ferent advertising channels.Comment: 9 pages, 9 figure