Abstract: Click-through rate prediction has played an important role in video recommendation systems. A video recommendation system can suggest media to users based on the results of click-through rate prediction. In this way, users may be more likely to click the videos recommended by platforms. However, given the volume and imbalance of data in some applications, the accuracy of click-through rate prediction may be very low. To improve the performance, this paper proposes an integrated approach by combining feature engineering with techniques from machine learning. In the first stage, the algorithm uses feature engineering to extract user, video, and combinational features from the original dataset. In the second stage, the algorithm predicts the click-through rate by employing supervised models of logistic regression, factorization machine, and gradient boosting decision tree combined with logistic regression. The experimental results illustrate that the prediction accuracy of the factorization machine model and the gradient boosting decision tree combined with logistic regression model are better than the logistic regression model. Moreover, the cross combination of user and video features can improve the accuracy of the click-through rate prediction.

Key words: click-through rate prediction, feature engineering, factorization machine, gradient boosting decision tree