Enhancing music recommendation algorithms using cultural metadata

In today's online commercial music marketplaces, a common requirement is to generate lists of artists that are "similar" to a given chosen artist. However, this is by no means a trivial task. A recent trend has been to tackle this challenge using sociocultural connotations rather than the traditional content-based audio or lyrics analysis. This article describes an enhancement to this approach that relies on the acquisition, filtering and condensing of unstructured, text-based information that can be found on the World Wide Web to recognize what the music community regards as "similar" artists. The beauty of this approach lies in its ability to access so-called "cultural metadata" (i.e., textual data about musical content) which is the aggregation of several independent - originally subjective - perspectives about a piece of music. The major focus of this work is the evaluation and enhancement of existing approaches in this area using filtering methods to increase their precision. A meaningful evaluation of the results is provided by a comparison with ground truth data.