Ephemeral Context to Support Robust and Diverse Music Recommendations

TL;DR

This paper introduces a method for music recommendation that uses multiple sensors and external data to capture detailed, momentary contexts, improving robustness and enabling novel content discovery despite sensor data issues.

Contribution

It presents a new approach leveraging diverse data sources to describe ephemeral contexts, overcoming limitations of fixed context models in music recommendation systems.

Findings

Enhanced context inference despite missing or faulty sensor data

Supports novel music content discovery based on rich contextual information

Improves robustness and diversity in music recommendations

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.