  An increasing amount of digital music is being published daily. Music
streaming services often ingest all available music, but this poses a
challenge: how to recommend new artists for which prior knowledge is scarce? In
this work we aim to address this so-called cold-start problem by combining text
and audio information with user feedback data using deep network architectures.
Our method is divided into three steps. First, artist embeddings are learned
from biographies by combining semantics, text features, and aggregated usage
data. Second, track embeddings are learned from the audio signal and available
feedback data. Finally, artist and track embeddings are combined in a
multimodal network. Results suggest that both splitting the recommendation
problem between feature levels (i.e., artist metadata and audio track), and
merging feature embeddings in a multimodal approach improve the accuracy of the
recommendations.
