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.