A mobile package recommendation method based on grid search combined with XGBoost model
Chengxin Zhang, Shengnan Zhang · 2023
The package recommendation has always been an important issue in the marketing of mobile operators, and machine learning provides a new solution for operators. Aiming at the problem that too many training times of dirty data in the existing methods lead to the reduction of prediction accuracy and the tedious manual setting of model parameters, this paper propose a model combining grid search and XGBoost, and use the exhaustive search method to find the parameter value with the highest accuracy in the validation set within the parameter range of the given XGBoost. Compared with Random Forest and XGBoost default parameter values, experiments show that the proposed model has higher prediction accuracy and can effectively avoids the error caused by manual adjustment of parameters.