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

Kun Gai; Xiaoqiang Zhu; Han Li; Kai Liu; Zhe Wang

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arXiv · 1704.05194

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

Kun Gai·Xiaoqiang Zhu·Han Li·Kai Liu·Zhe Wang

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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.

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BibTeX RIS

Kun Gai, Xiaoqiang Zhu, Han Li, Kai Liu, Zhe Wang. 2017-04-18. Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction. https://arxiv.org/abs/1704.05194

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