TV viewership data available at the individual set-top box level has enabled new methods for estimating the demographics of shows' audiences, but it is impossible to tell with certainty which household members are watching TV in multi-person households. We address this problem through four main...

Dimensionality Reduction (DR) is frequently employed in the predictive modeling process with the goal of improving the generalization performance of models. This paper takes a design science perspective on DR.We treat it as an important business analytics artifact and investigate its utility in...

The emergence of online paid crowdsourcing platforms, such as Amazon Mechanical Turk (AMT), presents us huge opportunities to distribute tasks to human workers around the world, on-demand and at scale. In such settings, online workers can come and complete tasks posted by a company, and work for...

The increasing availability of massive data on users' online behavior presents exciting opportunities for business analytics. In particular, if we could model the distributions of interests of visitors to webpages (or websites), we could apply the result to applications including site...

A main goal of online display advertising is to drive purchases (etc.) following ad engagement. However, there often are too few purchase conversions for campaign evaluation and optimization, due to low conversion rates, cold start periods, and long purchase cycles (e.g., with brand...

Recent studies show the remarkable power of information disclosed by users on social network sites to infer the users' personal characteristics via predictive modeling. In response, attention is turning increasingly to the transparency that sites provide to users as to what inferences are drawn...

This paper addresses the repeated acquisition of labels for data itemswhen the labeling is imperfect. We examine the improvement (or lackthereof) in data quality via repeated labeling, and focus especially onthe improvement of training labels for supervised induction. With theoutsourcing of...

This paper presents a detailed discussion of problem formulation and data representation issues in the design, deployment, and operation of a massive-scale machine learning system for targeted display advertising. Notably, the machine learning system itself is deployed and has been in continual...

Traditional event models underlying naive Bayes classifiers assume probability distributions that are not appropriate for binary data generated by human behaviour. In this work, we develop a new event model, based on a somewhat forgotten distribution created by Kenneth Ted Wallenius in 1963. We...

Many document classification applications require human understanding of the reasons for data-driven classification decisions: by managers, client-facing employees, and the technical team. Predictive models treat documents as data to be classified, and document data are characterized by very...