Enhancing mobile recommender systems with activity inference

Abstract

Today's mobile leisure guide systems give their users unprecedented help in finding places of interest. However, the process still requires significant user interaction, for example to specify preferences and navigate lists. While interaction is effective for obtaining desired results, learning the interaction pattern can be an obstacle for new users, and performing it can slow down experienced users. This paper describes how to infer a user's high-level activity automatically to improve recommendations. Activity is determined by interpreting a combination of current sensor data, models generated from historical sensor data, and priors from a large time-use study. We present an initial user study that shows an increase in prediction accuracy from 62% to over 77%, and discuss the challenges of integrating activity representations into a user model. © 2009 Springer Berlin Heidelberg.