The ever-increasing size and accessibility of vast music libraries has created a demand more than ever for machine learning systems that are capable of understanding and organizing this complex data. While this topic has received relatively little attention within the machine learning community, it has been an area of intense focus within the community of Music Information Retrieval (MIR), where significant progress has been made, but these problems remain far from solved.

Furthermore, the recommender systems community has made great progress in terms of collaborative feedback recommenders, but these approaches suffer strongly from the cold-start problem. As such, recommendation techniques often fall back on content-based machine learning systems, but defining musical similarity is extremely challenging as myriad features all play some role (e.g., cultural, emotional, timbral, rhythmic).

We seek to use this workshop to bring together a group of world-class experts to discuss these challenges and share them with the greater machine learning community. In addition to making progress on these challenges, we hope to engage the machine learning community with our nebulous problem space, and connect them with the many available datasets the MIR community has to offer (e.g., AcousticBrainz, Million Song Dataset), which offer near commercial scale to the academic research community.