Uncovering Personal and Context-Dependent Display Preferences in Mobile Newsreader App

Abstract

The smartphone has revolutionised the way we receive news, enabling on-demand, personalised content to be viewed in a range of different situations. Yet, while the content of the news is often adapted to the user’s preferences and the current environment (e.g. location), the actual interface of a mobile newsreader app often remains the same across users and contexts of use. In this work we first collect and examine real-world mobile news reading data to uncover the way contextual factors affect the perception of different aspects of the newsreader app interface, and then develop a method for modelling personalised context-dependent viewing preferences. Through a four-week long user study we demonstrate that our reinforcement and active learning-based personalisation approach leads to 26% higher user acceptance as compared to a generic context-aware mobile newsreader interface adaptation model.