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arXiv · 1708.02765

Ephemeral Context to Support Robust and Diverse Music Recommendations

Pavel Kucherbaev·Nava Tintarev·Carlos Rodriguez
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Abstract

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.

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Pavel Kucherbaev, Nava Tintarev, Carlos Rodriguez. 2017-08-09. Ephemeral Context to Support Robust and Diverse Music Recommendations. https://arxiv.org/abs/1708.02765

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