Ephemeral Context to Support Robust and Diverse Music Recommendations
This work aims to improve music recommendation systems for users by enabling more robust and diverse suggestions based on rich, momentary contexts, though it appears incremental as it builds on prior context-based approaches.
The paper tackled the problem of limited context-based music recommendations by proposing a method to use multiple sensors and external data sources to describe ephemeral context with a large number of states, addressing issues like inferring context from missing data and supporting novel content discovery.
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.