# Terminology

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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

### Embedding

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](https://developers.google.com/machine-learning/glossary#embeddings) representation of
the queries and items.