The Research Of Multivariate Logistic Regression Algorithm On Real-time Bidding

Real-time bidding is a process that completed by the demand side platform, the media and consumer. This process is a win-win-win, consumers can see the advertisement that they are interested, advertisers can be made into more precise, reduce cost, the demand side platform can make more advertising. Real-time bidding is a very complex and involved fields a wide range of work, in this paper, the real-time bidding proposes a algorithm based on logistic regression model. The real-time bidding integral algorithm is divided into two main aspects: data preprocessing and model training. The model training is divided into offline training audience choice model and logistic regression model. Part of data processing, log data from a variety of bidding, the negative part, by a kind of optimized, is of great advantage to deal with high-dimensional sparse data clustering algorithm in clustering, achieved good results, greatly reduce the number of the negative cases. Raise the proportion of positive and negative cases. Greatly reduces the length of training model, to improve the training effect. Model training, starting from the treated for log data, through the data preprocessing, feature extraction, incremental merging, sorting, dimension reduction, model, model validation these steps, logistic regression models. This model for a feature to the weight of the mapping file. Multivariate logistic regression model is a kind of supervised learning algorithms, the key point is how to extract data set the features of high degree of differentiation. Aiming at the problem of real-time bidding dual characteristics, through the analysis of experimental results, effectively improve the total score. And, in view of real-time bidding this particular scenario, with the introduction of offline audience model, good results have been achieved.