CTR prediction in real-world business is a difficult machine learning problem with large scale nonlinear sparse data. In this paper, we introduce an industrial strength solution with model named Large Scale Piece-wise Linear Model (LS-PLM). We formulate the learning problem with L1 and L2,1 regularizers, leading to a non-convex and non-smooth optimization problem. Then, we propose a novel algorithm to solve it efficiently, based on directional derivatives and quasi-Newton method. In addition, we design a distributed system which can run on hundreds of machines parallel and provides us with the industrial scalability. LS-PLM model can capture nonlinear patterns from massive sparse data, saving us from heavy feature engineering jobs. Since 2012, LS-PLM has become the main CTR prediction model in Alibaba's online display advertising system, serving hundreds of millions users every day.
@article{arxiv.1704.05194,
title = {Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction},
author = {Kun Gai and Xiaoqiang Zhu and Han Li and Kai Liu and Zhe Wang},
journal= {arXiv preprint arXiv:1704.05194},
year = {2017}
}