Over the past decade, smartphones have become indispensable personal mobile devices, experiencing a remarkable surge in software apps. These apps empower users to seamlessly connect with various internet services, such as social communication and online shopping. Accurately predicting smartphone app usage can effectively improve user experience and optimize resource utilization. However, existing models often treat app usage prediction as a classification problem, which suffers from issues of app usage imbalance and out-of-distribution (OOD) during deployment. To address these challenges, this paper proposes a novel click-through rate (CTR) ranking-based method for predicting app usage. By transforming the classification problem into a CTR problem, we can eliminate the negative impact of the app usage imbalance issue. To address the OOD issue during deployment, we generate the app click sequence and three types of discriminative features, which enable generalization on unseen apps. The app click sequence and the three types of features serve as inputs for training a CTR estimation model in the cloud, and the trained model is then deployed on the user's smartphone to predict the CTR for each installed app. The decision-making process involves ranking these CTR values and selecting the app with the highest CTR as the final prediction. Our method has been extensively tested with large-scale app usage data. The results demonstrate that our approach is able to outperform state-of-the-art methods, with improvements over 4.93% in top-3 accuracy and 6.64% in top-5 accuracy. It achieves approximately twice the accuracy in predicting apps with low usage frequencies in comparison to baseline methods. Our method has been successfully deployed on the app recommendation system of a leading smartphone manufacturer.