7,572 research outputs found
Multi-Touch Attribution Based Budget Allocation in Online Advertising
Budget allocation in online advertising deals with distributing the campaign
(insertion order) level budgets to different sub-campaigns which employ
different targeting criteria and may perform differently in terms of
return-on-investment (ROI). In this paper, we present the efforts at Turn on
how to best allocate campaign budget so that the advertiser or campaign-level
ROI is maximized. To do this, it is crucial to be able to correctly determine
the performance of sub-campaigns. This determination is highly related to the
action-attribution problem, i.e. to be able to find out the set of ads, and
hence the sub-campaigns that provided them to a user, that an action should be
attributed to. For this purpose, we employ both last-touch (last ad gets all
credit) and multi-touch (many ads share the credit) attribution methodologies.
We present the algorithms deployed at Turn for the attribution problem, as well
as their parallel implementation on the large advertiser performance datasets.
We conclude the paper with our empirical comparison of last-touch and
multi-touch attribution-based budget allocation in a real online advertising
setting.Comment: This paper has been published in ADKDD 2014, August 24, New York
City, New York, U.S.
An Integrated Framework for Competitive Multi-channel Marketing of Multi-featured Products
For any company, multiple channels are available for reaching a population in
order to market its products. Some of the most well-known channels are (a) mass
media advertisement, (b) recommendations using social advertisement, and (c)
viral marketing using social networks. The company would want to maximize its
reach while also accounting for simultaneous marketing of competing products,
where the product marketings may not be independent. In this direction, we
propose and analyze a multi-featured generalization of the classical linear
threshold model. We hence develop a framework for integrating the considered
marketing channels into the social network, and an approach for allocating
budget among these channels
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization
Creating impact in real-world settings requires artificial intelligence
techniques to span the full pipeline from data, to predictive models, to
decisions. These components are typically approached separately: a machine
learning model is first trained via a measure of predictive accuracy, and then
its predictions are used as input into an optimization algorithm which produces
a decision. However, the loss function used to train the model may easily be
misaligned with the end goal, which is to make the best decisions possible.
Hand-tuning the loss function to align with optimization is a difficult and
error-prone process (which is often skipped entirely).
We focus on combinatorial optimization problems and introduce a general
framework for decision-focused learning, where the machine learning model is
directly trained in conjunction with the optimization algorithm to produce
high-quality decisions. Technically, our contribution is a means of integrating
common classes of discrete optimization problems into deep learning or other
predictive models, which are typically trained via gradient descent. The main
idea is to use a continuous relaxation of the discrete problem to propagate
gradients through the optimization procedure. We instantiate this framework for
two broad classes of combinatorial problems: linear programs and submodular
maximization. Experimental results across a variety of domains show that
decision-focused learning often leads to improved optimization performance
compared to traditional methods. We find that standard measures of accuracy are
not a reliable proxy for a predictive model's utility in optimization, and our
method's ability to specify the true goal as the model's training objective
yields substantial dividends across a range of decision problems.Comment: Full version of paper accepted at AAAI 201
Pilot Clustering in Asymmetric Massive MIMO Networks
We consider the uplink of a cellular massive MIMO network. Since the spectral
efficiency of these networks is limited by pilot contamination, the pilot
allocation across cells is of paramount importance. However, finding efficient
pilot reuse patterns is non-trivial especially in practical asymmetric base
station deployments. In this paper, we approach this problem using coalitional
game theory. Each cell has its own unique pilots and can form coalitions with
other cells to gain access to more pilots. We develop a low-complexity
distributed algorithm and prove convergence to an individually stable coalition
structure. Simulations reveal fast algorithmic convergence and substantial
performance gains over one-cell coalitions and full pilot reuse.Comment: Published in Proc. of IEEE International Workshop on Signal
Processing Advances in Wireless Communications (SPAWC '15), 5 pages, 1
tables, 5 figure
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