Prediction of User's Purchase Intention Based on Machine Learning

In recent years, the use of machine learning methods to deal with the problem of user interest prediction has become a hot research direction in the field of electronic commerce. In the present stage, a naive Bayesian algorithm has the advantages of simple implementation and high classification efficiency. However, this method is too dependent on the distribution of samples in the sample space, and has the potential of instability. To this end, the decision tree method is introduced to deal with the problem of interest classification, and the innovative use of Localstorage technology in HTML5 to obtain the required the experimental data. Classification method uses the information entropy of the training data set to build the classification model, through the simple search of the classification model to complete the classification of unknown data items. Both theoretical analysis and experimental results show that the decision tree is used to deal with the problem of prediction of users' interests has obvious advantages in the efficiency and stability.