关键词: 转化率预估, 高效的梯度提升树, 基于区域的因子分解机, 混合模型, 高阶组合特征

Abstract: Many existing models of predicting advertising conversion rate lack research on the interaction among deeper features. Hence， a new hybrid model was proposed for this problem. High-level combination features were extracted using a light gradient boosting machine(LightGBM) model， and combining with the advantages of field-aware factorization machines(FFM) model. It can effectively process sparse data and predict the conversion rate. In order to verify the effectiveness and generalization ability of the hybrid model， the model was tested on two data sets for discussing the influence of parameters on model prediction results and was compared with other models. The experimental results show that the hybrid model is more accurate.

Key words: conversion rate prediction, light gradient boosting decision tree, field-aware factorization machines, hybrid model, high-level combination features