XGBoost-Based Method for Internet Advertising Conversion Rate Computer Intelligent Prediction Model

Internet advertising conversion rate is an important quantitative indicator for search engine service providers and advertisers, the realization of Internet advertising conversion rate prediction under the big data platform has strong theoretical research value and practical application value. Since the conversion of internet advertising is a small probability event under a large amount of data, therefore, in order to increase the advertising conversion rate prediction, a XGBoost-based method is proposed. Through the analysis of large-scale advertising conversion logs, we extracted data features and constructed data sets, then applied XGBoost algorithm to achieve advertising conversion rate prediction successfully. The experimental results show that compared with the traditional machine learning methods in industry, XGBoost has better prediction results than other methods under the same features extraction and data sets.