  We propose Meta-Prod2vec, a novel method to compute item similarities for
recommendation that leverages existing item metadata. Such scenarios are
frequently encountered in applications such as content recommendation, ad
targeting and web search. Our method leverages past user interactions with
items and their attributes to compute low-dimensional embeddings of items.
Specifically, the item metadata is in- jected into the model as side
information to regularize the item embeddings. We show that the new item
representa- tions lead to better performance on recommendation tasks on an open
music dataset.
