Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning

TL;DR

This paper presents a deep learning model trained on a large dataset of mobile user clicks to accurately predict subsequent user interactions, facilitating UI optimization and personalized experiences.

Contribution

It introduces a novel deep learning approach for modeling mobile user click sequences, leveraging large-scale data and contextual information for improved prediction accuracy.

Findings

Model achieves 48% top-1 accuracy in predicting next clicks.

Model achieves 71% top-3 accuracy, outperforming baselines.

Demonstrates potential for enhancing mobile UI design and user experience.

Abstract

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.