We design a novel method for automatically generating a playlist of recommended songs in the popular social music sharing application Spotify that are liked with high probability by a user. Our method employs multiple seed artists as an input that are obtained via the Facebook likes of artists and the listening history of songs of a Spotify user. First, we construct an input vector comprising all the artists that the user likes on Facebook and listens to in Spotify. Then, we search for other artists and bands related to them using EchoNest, an online state-of-the-art machine learning platform. We assign a score to every artist in the thereby obtained collection, based on the frequency of his/her appearance. Finally, we construct a playlist comprising randomly selected popular songs associated with the most frequently cited artists. We examine the recommendation performance of our algorithm by computing its WTF score (fraction of disliked songs) and novelty factor (fraction of new liked songs) on playlists generated for different seed input sizes. We observe that our approach substantially outperforms the built-in Spotify Radio recommender. On 30 song playlists, we are able to improve the WTF score by 49% and the novelty factor by 42%, on average. Due to its general design, our method is broadly applicable to a variety of personal content management scenarios.