关键词: 广告点击率, 逻辑回归, 因子分解机, FTRL算法

Abstract: In the current business scenario of online advertising, the linear model does not take into account these essential characteristics including the high-dimensional sparsity of advertising data and highly nonlinear association for features. Aiming at these problems, this paper introduces a feature extraction method based on the gradient boosting decision tree algorithm, and presents a factorization machine model based on the FTRL （Follow-The-Regularized-Leader） optimization algorithm. The FTRL algorithm can effectively learn the nonlinear relationship between features, enables different parameters to adapt to different learning rates and adds a mixed regularization term. The experimental results show that the factorization machine model based on the FTRL optimization algorithm can effectively improve the accuracy of advertising click through rate prediction.

Key words: advertising click through rate, logistic regression, factorization machine, Follow-The-Regularized-Leader（FTRL） algorithm