Multi-Perspective Modeling for Click Event Prediction

Abstract

We present our solutions to the RecSys Challenge 2015. We propose a multi-perspective modeling scheme for click event prediction, which involves techniques from sophisticated feature engineering for both click sessions and clicked items, classification based on gradient boosting tree, semi-supervised learning that utilizes information from test data, multi-class classification for different categories of sessions and items, classifier-based feature fusion from multi-class classification and in the end classifier ensembles from multiple models. We demonstrate that our scheme is intuitive, flexible and powerful for the Challenge tasks. Our solution based on the scheme achieves a score of 49,517.2 in the Challenge.