Predicting Clicks on Mobile Advertisements

Webb, Christina Fatima; Gorowara, Alexander Lochan

Student Work

Predicting Clicks on Mobile Advertisements

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We explored methods of improving upon Chitika, Inc.'s existing means of predicting which users would most probably click on an advertisement in a mobile application. We used machine learning algorithms, primarily Naive Bayes, that trained on demographic and behavioral information supplied by the user and his/her mobile device. After an exploratory phase, we gathered performance data using the AUC metric on twenty-eight different experimental conditions. When compared to the control condition, in which no preprocessing was performed on the data before being given to the unmodified Naive Bayes algorithm, we found only minor improvements in AUC.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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