Two-stage Cascaded Classifier for Purchase Prediction
This work addresses purchase prediction for e-commerce recommendation systems, but it appears incremental as it builds on existing methods like Random Forests and boosting.
The authors tackled purchase prediction in e-commerce by developing a two-stage cascaded classifier to predict buy sessions and purchased items, achieving a reasonable score on the RecSys Challenge 2015 dataset.
In this paper we describe our machine learning solution for the RecSys Challenge, 2015. We have proposed a time efficient two-stage cascaded classifier for the prediction of buy sessions and purchased items within such sessions. Based on the model, several interesting features found, and formation of our own test bed, we have achieved a reasonable score. Usage of Random Forests helps us to cope with the effect of the multiplicity of good models depending on varying subsets of features in the purchased items prediction and, in its turn, boosting is used as a suitable technique to overcome severe class imbalance of the buy-session prediction.