Using cultural metadata for artist recommendations

Our approach to generate recommendations for similar artists follows a recent tradition of authors tackling the problem not with content-based audio analysis. Following this novel procedure we rely on the acquisition, filtering and condensing of unstructured text-based information that can be found in the Web. The beauty of this approach lies in the possibility to access so-called cultural metadata that is the agglomeration of several independent -originally subjective - perspectives about music.

1. Introduction

Our approach to generate recommendations for similar artists follows a recent tradition of authors [1]–[4] tackling the problem not from the audio-based side of content analysis. Instead we rely on the acquisition, filtering and condensing of text-based information that can be found in the web. The beauty of this approach lies in the possibility to access so-called cultural metadata which is indeed the agglomeration of several independent - originally subjective - perspectives about music, i.e. artists. The advantages of such a technique are the following: 1

Incorporation of semantics: in contrast to content-based audio processing the usage of web reviews offers the possibility to access descriptive semantics about the musical work of an artist.

2

Instant availability: in contrast to collaborative filtering techniques such as used by commercial portals (e.g. Amazon) we have no bootstrapping phase in web-based approaches. After the release of new songs or albums immediate access to according reviews is possible.

3

Time-awareness: in contrast to the static representation of an audio-based approach the dynamics of changing cultural context, resp. artist relations are included in a web-based representation.