Localify.org: Locally-focus Music Artist and Event Recommendation

Cities with strong local music scenes enjoy many social and economic benefits. To this end, we are interested in developing a locally-focused artist and event recommendation system called Localify.org that supports and promotes local music scenes. In this demo paper, we describe both the overall system architecture as well as our core recommendation algorithm. This algorithm uses artist-artist similarity information, as opposed to user-artist preference information, to bootstrap recommendation while we grow the number of users. The overall design of Localify was chosen based on the fact that local artists tend to be relatively obscure and reside in the long tail of the artist popularity distribution. We discuss the role of popularity bias and how we attempt to ameliorate it in the context of local music recommendation.