The Recommended for You feature uses artificial intelligence to analyze your video library and your members' viewing habits, then surfaces personalized video suggestions directly in the catalog.
This creates a Netflix-style discovery experience that helps members find relevant content without browsing through every category manually.
📝 NOTE: This feature is currently available to select stores as part of an early access program. If you're interested in enabling AI Video Recommendations for your store, please reach out to your Customer Success Manager. It is currently available for the web. Mobile app support is coming soon.
How It Works
The AI Video Recommendations feature has two main components:
1. "Recommended for You" Catalog Row
A new dynamic category row appears in your catalog for signed-in members, showing a personalized selection of videos based on their viewing history and preferences. The row is placed prominently near the top of the catalog to maximize content discovery.
2. AI-Powered Related Videos
On individual video pages, the related videos section is enhanced with AI-powered suggestions. Instead of only showing videos from the same category, the system uses AI to find videos that are genuinely similar in content, style, and topic, helping members discover relevant content across your entire library.
Catalog Row Placement
When enabled, the Recommended for You row appears in the following order on the catalog page:
Featured Category (hero banner)
Recommended for You (NEW)
Continue Watching
My Library
Favorites
Admin-defined categories
ℹ️ INFO: The Recommended for You category row only appears for signed-in members who have some viewing history. Anonymous visitors and brand-new members without watch history will not see this row, similar to how Continue Watching and My Library behave.
What Powers the Recommendations
Behind the scenes, the system works in multiple stages:
AI Video Tagging: Each video in your library is automatically analyzed using AI. The system processes video metadata (titles, descriptions), subtitles, and visual content to generate detailed tags describing what each video is about, including topics, difficulty level, equipment used, style, and more.
Taste Profiles: As members watch videos, the system builds a taste profile for each member based on the types of content they engage with.
Smart Matching: The recommendation engine compares each member's taste profile against your tagged video library to surface the most relevant unwatched content.
💡 TIP: The more content a member watches, the better their recommendations become. Encourage new members to explore a few videos to help the system learn their preferences.
FAQs
How many videos appear in the Recommended for You row?
How many videos appear in the Recommended for You row?
The row displays a selection of recommended videos, similar in size to other catalog rows like Continue Watching.
Can I manually curate what appears in the Recommended for You row?
Can I manually curate what appears in the Recommended for You row?
No. The row is entirely powered by AI based on each member's individual viewing behavior. Every member sees a different set of recommendations.
Will members with no watch history see recommendations?
Will members with no watch history see recommendations?
Members need some viewing history for the system to build a taste profile. New members without watch history will not see the Recommended for You row until they've watched some content.
Does this replace the existing Related Videos section?
Does this replace the existing Related Videos section?
No, it enhances it. With AI Video Recommendations active on your store, the Related Videos section on video pages uses AI to find more relevant matches across your entire library, rather than relying solely on shared categories.
Does this work on mobile and TV apps?
Does this work on mobile and TV apps?
The Recommended for You catalog row is currently available on the web only. Mobile app support is being developed and will follow shortly. TV app support has not been announced yet.
Will this affect my catalog's performance or loading speed?
Will this affect my catalog's performance or loading speed?
No. The recommendations are computed in the background and served efficiently. There is no noticeable impact on page load times.
💬 As this feature is in early access, the behavior and availability described here may evolve as we gather feedback and expand the rollout.