Rushed or Relaxed? – How the Situation on the Road Influences the Driver’s Preferences for Music Tracks

In context-aware recommender systems, the dependency of the user’s ratings on factors that describe important aspects of the recommendation context is used to provide more relevant recommendations. Individual users may be influenced differently by the same set of contextual factors. By understanding this kind of dependency between the user’s ratings (evaluations) and context, it is possible to identify user profiles and use them to predict precisely the user ratings for items to be recommended. In this paper, we present our methodology to identify user profiles in a corpus of ratings for music tracks. These ratings were collected in a user study, which simulated typical situations that occur while driving a car. We present the findings derived from the data, and argue that it is feasible to distinguish different typologies of users from the ratings they give to music tracks in specific contexts.