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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 appropriateembeddingrepresentation of
the queries and items.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25 UTC."],[[["\u003cp\u003eRecommendation systems predict user preferences by suggesting relevant items like apps or videos.\u003c/p\u003e\n"],["\u003cp\u003eThese systems leverage user data, including past interactions and contextual information, to personalize recommendations.\u003c/p\u003e\n"],["\u003cp\u003eEmbeddings are mathematical representations of queries and items, enabling the system to identify similarities and make predictions.\u003c/p\u003e\n"]]],[],null,["# Terminology\n\n\u003cbr /\u003e\n\nBefore we dive in, there are a few terms that you should know:\n\n### Items (also known as documents)\n\nThe entities a system recommends. For the Google Play store, the items are apps\nto install. For YouTube, the items are videos.\n\n### Query (also known as context)\n\nThe information a system uses to make recommendations. Queries can be a\ncombination of the following:\n\n- user information\n - the id of the user\n - items that users previously interacted with\n- additional context\n - time of day\n - the user's device\n\n### Embedding\n\nA mapping from a discrete set (in this case, the set of queries, or the set of\nitems to recommend) to a vector space called the embedding space. Many\nrecommendation systems rely on learning an appropriate\n[embedding](/machine-learning/glossary#embeddings) representation of\nthe queries and items."]]
