Learning User Behaviors for Advertisements Click Prediction

Predicting potential advertisement clicks of users are important for advertisement recommendation, advertisement placement, presentation pricing, and so on. In this paper, several machine learning algorithms including conditional random fields (CRF), support vector machines (SVM), decision tree (DT) and backpropagation neural networks (BPN) are developed to learn user’s click behaviors from advertisement search and click logs. In addition, four levels of features are extracted to represent user search and click intents. Given a user’s search session and a query, machine learning algorithms along with different features are proposed to predict if the user will click advertisements displayed for the query. We further study the impact of feature selection algorithms on the prediction models. Random subspace (RS), F-score (FS) and information gain (IG) are employed to search for a predictive subset of features. The experiments show that CRF model with the random subspace feature selection algorithm achieves the best performance.