17,410 research outputs found
Predicting customer's gender and age depending on mobile phone data
In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain
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
Affinity Paths and Information Diffusion in Social Networks
Widespread interest in the diffusion of information through social networks
has produced a large number of Social Dynamics models. A majority of them use
theoretical hypothesis to explain their diffusion mechanisms while the few
empirically based ones average out their measures over many messages of
different content. Our empirical research tracking the step-by-step email
propagation of an invariable viral marketing message delves into the content
impact and has discovered new and striking features. The topology and dynamics
of the propagation cascades display patterns not inherited from the email
networks carrying the message. Their disconnected, low transitivity, tree-like
cascades present positive correlation between their nodes probability to
forward the message and the average number of neighbors they target and show
increased participants' involvement as the propagation paths length grows. Such
patterns not described before, nor replicated by any of the existing models of
information diffusion, can be explained if participants make their pass-along
decisions based uniquely on local knowledge of their network neighbors affinity
with the message content. We prove the plausibility of such mechanism through a
stylized, agent-based model that replicates the \emph{Affinity Paths} observed
in real information diffusion cascades.Comment: 11 pages, 7 figure
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