  While prior work on context-based music recommendation focused on fixed set
of contexts (e.g. walking, driving, jogging), we propose to use multiple
sensors and external data sources to describe momentary (ephemeral) context in
a rich way with a very large number of possible states (e.g. jogging fast along
in downtown of Sydney under a heavy rain at night being tired and angry). With
our approach, we address the problems which current approaches face: 1) a
limited ability to infer context from missing or faulty sensor data; 2) an
inability to use contextual information to support novel content discovery.
