Exploiting Twitter's Collective Knowledge for Music Recommendations

Abstract

Twitter is the largest source of public opinion and also contains a vast amount of information about its users’ music favors or listening behaviour. However, this source has not been exploited for the recommendation of music yet. In this paper, we present how Twitter can be facilitated for the creation of a data set upon which music recommendations can be computed. The data set is based on microposts which were automatically generated by music player software or posted by users and may also contain further information about audio tracks.