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What are recommendations?
How does YouTube know what video you might want to watch next? How does the
Google Play Store pick an app just for you? Magic? No, in both cases, an
ML-based recommendation model determines how similar videos and
apps are to other things you like and then serves up a recommendation.
Two kinds of recommendations are commonly used:
home page recommendations
related item recommendations
Homepage recommendations
Homepage recommendations are personalized to a user based on their known
interests. Every user sees different recommendations.
If you go to the Google Play Apps homepage, you may see something like this:
Related item recommendations
As the name suggests, related items are recommendations similar to a
particular item. In the Google Play apps example, users looking at a page for
a math app may also see a panel of related apps, such as other math or science
apps.
Why recommendations?
A recommendation system helps users find compelling content in a large corpus.
For example, the Google Play Store provides millions of apps, while YouTube
provides billions of videos. More apps and videos are added every day. How can
users find new and compelling content? Yes, one can use search to access
content. However, a recommendation engine can display items that users might
not have thought to search for on their own.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-25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 models predict user preferences by analyzing similarities between items and past user interactions to suggest relevant content.\u003c/p\u003e\n"],["\u003cp\u003eTwo common recommendation types are homepage recommendations (personalized to individual users) and related item recommendations (similar to a specific item being viewed).\u003c/p\u003e\n"],["\u003cp\u003eRecommendation systems help users discover new and engaging content within vast collections like Google Play and YouTube, going beyond search functionality.\u003c/p\u003e\n"],["\u003cp\u003eRecommendations significantly influence user behavior, driving a substantial portion of app installs and video watch time on these platforms.\u003c/p\u003e\n"]]],[],null,["# Recommendations: what and why?\n\n\u003cbr /\u003e\n\nWhat are recommendations?\n-------------------------\n\nHow does YouTube know what video you might want to watch next? How does the\nGoogle Play Store pick an app just for you? Magic? No, in both cases, an\nML-based recommendation model determines how similar videos and\napps are to other things you like and then serves up a recommendation.\nTwo kinds of recommendations are commonly used:\n\n- home page recommendations\n- related item recommendations\n\nHomepage recommendations\n------------------------\n\nHomepage recommendations are personalized to a user based on their known\ninterests. Every user sees different recommendations.\n\nIf you go to the Google Play Apps homepage, you may see something like this: \n\nRelated item recommendations\n----------------------------\n\nAs the name suggests, **related items** are recommendations similar to a\nparticular item. In the Google Play apps example, users looking at a page for\na math app may also see a panel of related apps, such as other math or science\napps.\n\nWhy recommendations?\n--------------------\n\nA recommendation system helps users find compelling content in a large corpus.\nFor example, the Google Play Store provides millions of apps, while YouTube\nprovides billions of videos. More apps and videos are added every day. How can\nusers find new and compelling content? Yes, one can use search to access\ncontent. However, a recommendation engine can display items that users might\nnot have thought to search for on their own.\n| **Did you know?**\n|\n| - 40% of app installs on Google Play come from recommendations.\n| - 60% of watch time on YouTube comes from recommendations."]]
