Before we dive in, there are a few terms that you should know:

Items (also known as documents)

The entities a system recommends. For the Google Play store, the items are apps to install. For YouTube, the items are videos.

Query (also known as context)

The information a system uses to make recommendations. Queries can be a combination of the following:

  • user information
    • the id of the user
    • items that users previously interacted with
  • additional context
    • time of day
    • the user's device


A mapping from a discrete set (in this case, the set of queries, or the set of items to recommend) to a vector space called the embedding space. Many recommendation systems rely on learning an appropriate embedding representation of the queries and items.