Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning
This work addresses UI optimization for mobile users by improving click prediction, though it is incremental as it applies deep learning to a specific domain.
The paper tackled predicting the next element a user clicks on a mobile device using a deep learning model that incorporates click history, UI structure, and context, achieving 48% top-1 and 71% top-3 accuracy on a dataset of over 20 million clicks from 4,000 users, significantly outperforming baselines.
Modeling tap or click sequences of users on a mobile device can improve our understandings of interaction behavior and offers opportunities for UI optimization by recommending next element the user might want to click on. We analyzed a large-scale dataset of over 20 million clicks from more than 4,000 mobile users who opted in. We then designed a deep learning model that predicts the next element that the user clicks given the user's click history, the structural information of the UI screen, and the current context such as the time of the day. We thoroughly investigated the deep model by comparing it with a set of baseline methods based on the dataset. The experiments show that our model achieves 48% and 71% accuracy (top-1 and top-3) for predicting next clicks based on a held-out dataset of test users, which significantly outperformed all the baseline methods with a large margin. We discussed a few scenarios for integrating the model in mobile interaction and how users can potentially benefit from the model.