  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 $L_1$ and $L_{2,1}$
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.
