  We explore the task of local music recommendation: provide listeners with
personalized playlists of relevant tracks by artists who play most of their
live events within a small geographic area. Most local artists tend to be
obscure, long-tail artists and generally have little or no available user
preference data associated with them. This creates a cold-start problem for
collaborative filtering-based recommendation algorithms that depend on large
amounts of such information to make accurate recommendations. In this paper, we
compare the performance of three standard recommender system algorithms
(Item-Item Neighborhood (IIN), Alternating Least Squares for Implicit Feedback
(ALS), and Bayesian Personalized Ranking (BPR)) on the task of local music
recommendation using the Million Playlist Dataset. To do this, we modify the
standard evaluation procedure such that the algorithms only rank tracks by
local artists for each of the eight different cities. Despite the fact that
techniques based on matrix factorization (ALS, BPR) typically perform best on
large recommendation tasks, we find that the neighborhood-based approach (IIN)
performs best for long-tail local music recommendation.
