PUBLICATIONS

ABSTRACT

Predicting Shopping Behavior with Mixture of RNNs

We compare two machine learning approaches for early predic- tion of shoppers’ behaviors, leveraging features from clickstream data generated during live shopping sessions. Our baseline is a mixture of Markov models to predict three outcomes: purchase, abandoned shopping cart, and browsing-only. We then experiment with a mixture of Recurrent Neural Networks. When sequences are truncated to 75% of their length, a relatively small feature set predicts purchase with an F-measure of 0.80 and browsing-only with an F-measure of 0.98. We also investigate an entropy-based decision procedure.