Characterizing User Skills from Application Usage Traces with Hierarchical Attention Recurrent Networks

Predicting users’ proficiencies is a critical component of AI-powered personal assistants. This article introduces a novel approach for the prediction based on users’ diverse, noisy, and passively generated application usage histories. We propose a novel bi-directional recurrent neural network with hierarchical attention mechanism to extract sequential patterns and distinguish informative traces from noise. Our model is able to attend to the most discriminative actions and sessions to make more accurate and directly interpretable predictions while requiring 50× less training data than the state-of-the-art sequential learning approach. We evaluate our model with two large scale datasets collected from 68K Photoshop users: a digital design skill dataset where the user skill is determined by the quality of the end products and a software skill dataset where users self-disclose their software usage skill levels. The empirical results demonstrate our model’s superior performance compared to existing user representation learning techniques that leverage action frequencies and sequential patterns. In addition, we qualitatively illustrate the model’s significant interpretative power. The proposed approach is broadly relevant to applications that generate user time-series analytics.