| Resumo: | Mobile performance marketing is a growing industry due to the massive adoption of smartphones and tablets. In this paper, we explore Deep Multilayer Perceptrons (MLP) to predict the Conversion Rate (CVR) of mobile users that are redirected to ad campaigns (i.e., if there will be a sale). We analyze recent real-world big data provided by a global mobile marketing company. Using a realistic rolling window validation, we conducted several experiments with different datasets (two sampling and two data traffic modes), in which we measure both the predictive binary classification performance and the computational effort. The modeling experiments include: two data preprocessing methods, the popular one-hot encoding and a proposed Percentage Categorical Pruning (PCP); and two MLP learning modes, offline (reset) and online (reuse). Overall, competitive classification results were achieved by the PCP transform and the two MLP learning modes, producing real-time predictions and comparing favorably against a Convolutional Neural Network and a Logistic Regression.
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