Arts, Humanities, and Complex Networks

Music Recommendation Networks

by Pedro Cano

Abstract

Nowadays it is possible to access millions of music tracks. In order to ease users to search and discover the music, a number of recommendation systems have been proposed. We analyze the topology of several commercial recommendation systems from a networks perspective.  We observe  structural properties that provide some hints on searchability and behavior of music recommendation systems. Is the Long Tail truly exploited? How much of the network structure is due to actual musical similarity and how much to other network growth processes? The properties derived allow us to compare different recommendation approaches: Expert-based, collaborative filtering and content-based.

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