Targeted Advertising Based on Browsing History
Table 5: Gender prediction accuracy of SVM against other baseline classifiers (%)
['Models', 'Demo20', 'Demo100']
All the classifiers have been implemented using scikit-learn toolkit Pedregosa et al. For the neural network, one hidden layer perceptron classifier has been used. Random forest and logistic regression are implemented using base classifiers in the toolkit. Default settings are used for most parameters. The most important hyper-parameters, like regularization strength of logistic regression, the number of maximal features and estimators of the random forest, and the number of hidden nodes of the neural network are determined using grid search and cross-validation. Random forest performs rather poor in this task, which should be expected from the noisy characteristic of web data. The fact that a linear SVM achieves rather satisfactory performance demonstrates that the feature representations of audiences are effective and informative.