基于淘宝广告数据的点击概率预估模型研究  

Research on Click Probability Estimation Model Based on Taobao Advertising Data

作　　者：高巍 张奥南 李大舟 王淮中 GAO Wei;ZHANG Ao-nan;LI Da-zhou;WANG Huai-zhong(Shenyang University of Chemical Technology,Shenyang 110142,China)

机构地区：[1]沈阳化工大学计算机科学与技术学院,辽宁沈阳110142

出　　处：《沈阳化工大学学报》2022年第5期461-467,共7页Journal of Shenyang University of Chemical Technology

基　　金：辽宁省教育厅科学技术研究项目(L2016011);辽宁省教育厅科学研究项目(LQ2017008);辽宁省博士启动基金项目(201601196)。

摘　　要：提出一种将压缩交互网络、因子分解机模型和神经网络3种模型相结合构成的因子交互全连接网络预估模型预测淘宝广告数据中用户对广告的点击概率.利用TensorFlow搭建整个算法模型,并使用淘宝广告展示数据集进行训练.最终训练出的模型可以得到数据中用户对任意一条广告的点击概率.采用曲线下面积(AUC)与对数损失函数(Logloss)值作为模型的评价指标,得到的结果与LR、FM、DeepFM等点击概率预估模型进行对比,AUC值提高了0.05,Logloss值降低了0.04,效果得到明显提升.This paper presents a factor interactive fully connected network prediction model which combines compressed interactive network,factor decomposition machine model and neural network to predict the click probability of users in Taobao advertising data.Tensorflow is used to build the whole algorithm model,and Taobao advertising display data set is used for training.Finally,the trained model can get the click probability of any advertisement in the data.The area under the curve(AUC)and log loss function(Logloss)values are used as the evaluation indicators of the model.The results obtained are compared with LR,FM,DeepFM and other click probability prediction models.The AUC value is increased by 0.05,and the Logloss value is reduced by 0.04.The effect is significantly improved.

关 键 词：因子交互全连接网络 因子分解机 压缩交互网络 神经网络 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]