436 research outputs found
Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions
One of the most challenging problems in computational advertising is the
prediction of click-through and conversion rates for bidding in online
advertising auctions. An unaddressed problem in previous approaches is the
existence of highly non-uniform misprediction costs. While for model evaluation
these costs have been taken into account through recently proposed
business-aware offline metrics -- such as the Utility metric which measures the
impact on advertiser profit -- this is not the case when training the models
themselves. In this paper, to bridge the gap, we formally analyze the
relationship between optimizing the Utility metric and the log loss, which is
considered as one of the state-of-the-art approaches in conversion modeling.
Our analysis motivates the idea of weighting the log loss with the business
value of the predicted outcome. We present and analyze a new cost weighting
scheme and show that significant gains in offline and online performance can be
achieved.Comment: First version of the paper was presented at NIPS 2015 Workshop on
E-Commerce: https://sites.google.com/site/nips15ecommerce/papers Third
version of the paper will be presented at AdKDD 2017 Workshop:
adkdd17.wixsite.com/adkddtargetad201
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
Lift-Based Bidding in Ad Selection
Real-time bidding (RTB) has become one of the largest online advertising
markets in the world. Today the bid price per ad impression is typically
decided by the expected value of how it can lead to a desired action event
(e.g., registering an account or placing a purchase order) to the advertiser.
However, this industry standard approach to decide the bid price does not
consider the actual effect of the ad shown to the user, which should be
measured based on the performance lift among users who have been or have not
been exposed to a certain treatment of ads. In this paper, we propose a new
bidding strategy and prove that if the bid price is decided based on the
performance lift rather than absolute performance value, advertisers can
actually gain more action events. We describe the modeling methodology to
predict the performance lift and demonstrate the actual performance gain
through blind A/B test with real ad campaigns in an industry-leading
Demand-Side Platform (DSP). We also discuss the relationship between
attribution models and bidding strategies. We prove that, to move the DSPs to
bid based on performance lift, they should be rewarded according to the
relative performance lift they contribute.Comment: AAAI 201
A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search
Sponsored search is an important monetization channel for search engines, in
which an auction mechanism is used to select the ads shown to users and
determine the prices charged from advertisers. There have been several pieces
of work in the literature that investigate how to design an auction mechanism
in order to optimize the revenue of the search engine. However, due to some
unrealistic assumptions used, the practical values of these studies are not
very clear. In this paper, we propose a novel \emph{game-theoretic machine
learning} approach, which naturally combines machine learning and game theory,
and learns the auction mechanism using a bilevel optimization framework. In
particular, we first learn a Markov model from historical data to describe how
advertisers change their bids in response to an auction mechanism, and then for
any given auction mechanism, we use the learnt model to predict its
corresponding future bid sequences. Next we learn the auction mechanism through
empirical revenue maximization on the predicted bid sequences. We show that the
empirical revenue will converge when the prediction period approaches infinity,
and a Genetic Programming algorithm can effectively optimize this empirical
revenue. Our experiments indicate that the proposed approach is able to produce
a much more effective auction mechanism than several baselines.Comment: Twenty-third International Conference on Artificial Intelligence
(IJCAI 2013
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
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