Recommender systems are popular social web tools, as they address the information overload problem and provide personalization of results [1]. This paper presents a large-scale collaborative approach to the crucial part in the playlist recommendation process: next song recommendation. We show that a simple markov-chain based algorithm improves performance compared to baseline models when neither content, nor user metadata is available. The lack of content-based features makes this task particularly hard. 