Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction

Code

• github.com/shenweichen/DeepCTR
  tf★ 8,006
• github.com/shenweichen/DeepCTR-Torch
  pytorch★ 3,394
• github.com/UlionTse/mlgb
  pytorch★ 1,049

Abstract

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.

Tasks

Reproductions