Design and Evaluation of a Probabilistic Music Projection Interface

We describe the design and evaluation of a probabilistic interface for music exploration and casual playlist generation. Predicted subjective features, such as mood and genre, inferred from low-level audio features create a 34- dimensional feature space. We use a nonlinear dimensionality reduction algorithm to create 2D music maps of tracks, and augment these with visualisations of probabilistic mappings of selected features and their uncertainty. We evaluated the system in a longitudinal trial in users’ homes over several weeks. Users said they had fun with the interface and liked the casual nature of the playlist generation. Users preferred to generate playlists from a local neighbourhood of the map, rather than from a trajectory, using neighbourhood selection more than three times more often than path selection. Probabilistic highlighting of subjective features led to more focused exploration in mouse activity logs, and 6 of 8 users said they preferred the probabilistic highlighting mode

User Interface Plasticity: Model Driven Engineering to the Limit!

Keynote paper.International audienceTen years ago, I introduced the notion of user interface plasticity to denote the capacity of user interfaces to adapt, or to be adapted, to the context of use while preserving usability. The Model Driven Engineering (MDE) approach, which was used for user interface generation since the early eighties in HCI, has recently been revived to address this complex problem. Although MDE has resulted in interesting and convincing results for conventional WIMP user interfaces, it has not fully demonstrated its theoretical promises yet. In this paper, we discuss how to push MDE to the limit in order to reconcile high-level modeling techniques with low-level programming in order to go beyond WIMP user interfaces