Research On Click-through Rates Predicting Of Display Advertising

With the rapid development of the Internet,online advertising acts a pivotal part in the Internet in our daily life and it has become the most popular approach to do brand promotion and product marketing for the advertis er.Accurate click-through rate(CTR)prediction is the most important part of online advertising.Improving the accuracy of the ads' CTR estimation can not only benefit to advertisers,but also improve user experience.Many traditional click through rate prediction methods,such a s logistic regression,have been applied to advertising click rate prediction system and achieved good results.Furthermore,it has been large-scale deployed in the industry.Recently,the deep learning technology has achieved great success in multiply fields of Natural Language Processing and Computer Vision,such as Textual Entailment,Text Summarization,Image Generation and so on.Meanwhile,a number of deep learning models right now have been used in personalized recommender system and CTR prediction and their model structures are similar.Both of them reduce the dimension of the feature by vectorization,then utilize nonlinear operation to extract the feature combination,and calculate nonlinear relationship between the features and the click rate through by neural network.The content of this paper of the following three main aspects:(1)Ensemble Learning by multiple traditional machine learning models based CTR model.We first do feature engineering on two large-scale real-world display advertising datasets manually and extract high-order combination feature by GBDT.Then we calculate CTR by mature machine learning models such as logistic regression and factorization machine.Then we utilize ensemble learning base on multiple single models.Finally,we calculate the result of ensemble learning method.(2)Advance deep learning model based CTR model.We use deep neural network and recurrent neural network to do click-through rate prediction.We try to combine the features extracted from feature engineering and get the input of deep neural network through feature hashing and feature connection.Finally,we calculate the result of advance deep learning model.(3)Multi-Embedding deep model based CTR model.We propose a novel CTR predicting model,Multi-Embedding Deep Model.We implement deep neural network based and convolutional neural network based traditional multi-embedding deep model,and also implement deep neural network based and convolutional neural network based bilinear multi-embedding deep model.which we utilize bilinear matrix to do feature interactions instead of factorization machines.We design a system to address the cold-start problem for static data set by combining clustering method and marking rare embedding vectors met hod.We evaluate the proposed model on IPin You and Avazu datasets,two large-scale real-world display advertising datasets.Experimental results show that the model can improve the estimation performance of ads' click-through rate effectively.