Application Research Of Advertisement Click-through-rate Prediction Based On Neural Network And Field-aware Factorization Machine

Calculating advertising is an emerging method of Internet profit,which has become one of the main traffic monetization methods of Internet companies.Advertising CTR estimation,as a core issue in the field of calculating advertising,has played an important role in the precise delivery of online advertising.Through a recent study and summary of a series of ad click rate estimation models based on traditional machine learning and deep learning,it is found that combining traditional machine learning models with deep learning models can greatly improve the problem of ad click rate estimation.AUC indicator.Among existing advertising C TR estimation models,the Deem model is a commonly used deep learning-based CTR estimation model.Among them,the factorization machine module of the Deep Fm model is used to extract the low-order features of the data,and the neural network module is used to extract the high-order features of the data,but when the factorization machine model is used to extract the second-order combined features,the same feature has different features.It is obviously unreasonable to map the same hidden vector in the combination.Based on this,when the click-through rate prediction model designed in the paper is used to extract low-order features,the factor decomposition machine model is modified to a field-aware factor decomposition machine model to solve the problem that the same feature is mapped to the same hidden vector in different feature combinations.This problem makes the extraction of low-order features more reasonable.For the neural network module,based on the fully connected network in the Deep FM model,a regularization method is introduced to enhance the robustness of the model and prevent the network from overfitting.In addition,for the extraction of user historical behavior characteristics,the click rate estimation model designed in the paper adds the Word2 Vec module to train the user's historical click behavior sequence to reflect the preferences of different users.Based on the above three improvements and optimizations,the paper designs an ad click rate estimation algorithm model based on neural network and field perception factor decomposition machine.In terms of feature engineering,a complete feature engineering solution was designed in the field of advertising click-through rate estimation,covering four major feature groups: original features,historical statistical features,statistical features of the prediction day,and user historical behavior sequence features.By analyzing and comparing the experimental results of different models on the ad click rate estimation data set provided by the 2019 Huawei DIGIX Algorithm Innovation Contest,it is found that the model designed in the paper has achieved a significant improvement on the AUC index.It can be considered that the advertising click-through rate estimation model proposed in the paper has certain application prospects and can effectively improve the accuracy of the advertisement click-through rate estimation system.