ContextPlay
Evaluating User Control for Context-Aware Music Recommendation
Yucheng Jin (Katholieke Universiteit Leuven)
Nyi Nyi Htun (Katholieke Universiteit Leuven)
N. Tintarev (TU Delft - Web Information Systems)
Katrien Verbert (Katholieke Universiteit Leuven)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Music preferences are likely to depend on contextual characteristics such as location and activity. However, most recommender systems do not allow users to adapt recommendations to their current context. We therefore built ContextPlay, a context-aware music recommender that enables user control for both contextual characteristics and music preferences. By conducting a mixed-design study (N=114) with four typical scenarios of music listening, we investigate the effect of controlling contextual characteristics in a music recommender system on four aspects: perceived quality, diversity, effectiveness, and cognitive load. Compared to our baseline which only allows to specify music preferences, having additional control for context leads to higher perceived quality and does not increase cognitive load. We also find that the contexts of mood, weather, and location tend to influence user perception of the system. Moreover, we found that users are more likely to modify contexts and their profile during relaxing activities.