AI-generated Key Takeaways
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Recommendation models predict user preferences by analyzing similarities between items and past user interactions to suggest relevant content.
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Two common recommendation types are homepage recommendations (personalized to individual users) and related item recommendations (similar to a specific item being viewed).
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Recommendation systems help users discover new and engaging content within vast collections like Google Play and YouTube, going beyond search functionality.
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Recommendations significantly influence user behavior, driving a substantial portion of app installs and video watch time on these platforms.
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.