Recommendations

Recommendations in Learn Amp help learners discover relevant content based on their interests, activity, and learning history. The platform uses intelligent algorithms to surface content that matches each user's profile, making it easier for learners to find valuable resources without searching.

Recommendations appear in various places throughout the platform, including Dashboard widgets, content pages, and the Discover page.

Learn Amp's recommendation engine works by analysing:

• User activity: Content viewed, started, and completed

• Tags and categories: Matching content tags to user interests

• Similar learners: What users with similar profiles have engaged with

• Content relationships: Items that are frequently viewed together

The recommendation engine uses several factors to surface the most relevant content:

💡 Tip: The more you interact with content, the better your recommendations become. Rate items, complete learning, and add interest tags to improve your suggestions.

For recommendations to work effectively, your platform needs:

• Content tagged with relevant topics and categories

• Users with some learning activity history

• The Recommendations Channel or widget configured (optional)

1. Navigate to your Dashboard to see the Recommended for You widget

2. Browse the Discover page to explore trending and recommended content

3. View any Item page to see More Like This suggestions

4. To personalise your recommendations, click on your avatar, then select Settings

5. Look for Recommendations to add interest tags and adjust filters

1. Navigate to Settings in the sidebar

2. Select Content from the settings menu

3. Click Recommendations to configure platform-wide settings

4. Toggle recommendations visibility and set the display position

5. Add the Recommendations widget to Dashboards for your users

Q: How is the 'More Like This' content generated?
The 'More Like This' section analyses the tags, categories, and metadata of the current item to find similar content. It also considers what other learners viewed after viewing the same item.

Q: Can I control what appears in recommendations?
Recommendations are primarily algorithm-driven, but you can influence them by properly tagging content and ensuring your content library is well-organised with clear categories.

Q: Why aren't recommendations showing for a user?
New users or those with limited activity history may see fewer recommendations. As they interact with more content, the recommendations become more personalised.

Q: Can I feature specific content in recommendations?
For curated content, use the Featured Channel widget instead of relying on algorithmic recommendations.

Q: How can I add the Recommendations widget to a Dashboard?
On your Dashboard, click Add Widget, search for "Recommendations", click on the widget, optionally rename it, then click Add Widget. The widget shows content based on user tags (if set) or based on ratings and creation date.

Q: How does the 'Recommended for you' widget configure content?
If a user has skills or tags set, the widget shows content matching those tags. If the user has no tags, it looks at all "Entire company" visibility items and shows content with high ratings and recently added content.

Q: What does the Recommendations widget use to suggest content?
The system uses Tags, Skills, and keywords in titles and descriptions. It will not recommend:

• Content that has been set as a task

• Content where there is a Started or Completed activity

• Library content that doesn't have "Entire Company" visibility

Q: Can users personalise their own recommendations?
Yes! Users can click on their avatar, then go to Settings and look for Recommendations to add interest tags and set filters for content type, rating, and price.