Personalized Music Recommendation in a Mobile Environment

Abstract

Addressing the spiraling amount of music and video consumption via streaming services, in particular on mobile devices, we present a music player application for the Android platform, which employs a hybrid approach to generate a list of track recommendations for a user. We propose and evaluate two different algorithms, namely a content-based algorithm and an approach that exploits social similarity. While the former is based on rhythm features, the latter exploits "related videos" relations from YouTube. We show via a user questionnaire that recommendation results based on content slightly, but statistically significantly, outperform the social approach. Given that full audio content is not available immediately in a streaming environment, however, we suggest a hybrid, dynamic approach to music recommendation. Playlists are created as a linear, user-adjustable mixture of both content and social similarity. They are offered to the user via an Android application dubbed "Beat Commander". Besides displaying the results of the playlist generation approach as text, the player features a dynamic visualization of the playlist, using a version of Sammon's mapping.