---
title: "AI-powered recommendations"
slug: "ai-powered-recommendations"
updated: 2025-07-22T10:52:18Z
published: 2025-07-22T11:18:58Z
canonical: "totara.help/ai-powered-recommendations"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://totara.help/llms.txt
> Use this file to discover all available pages before exploring further.

# AI-powered recommendations

## Enhanced content discovery with AI-powered recommendations

We are integrating Totara’s AI-driven recommendation engine directly into the learning catalogue via the **Recommended for you** block. This block includes content types that are personalised and recommended for the user, i.e. **Courses** (default), **Micro-learning**, **Workspaces**, **Programs** and **Certifications** (that have self-enrolment enabled).

The **Recommendation engine** transforms interaction data, user attributes, and content metadata into intelligent recommendation for users. It supports different use cases depending on available data and personalisation needs.

## How are recommendations determined?

Totara’s Recommendation engine works a bit like a smart assistant, it watches what you and other users interact with, learns from it, and suggests the most relevant content based on those actions.

### Interaction-based scoring

- Each positive user interaction (e.g., views, ratings, completions) gives content a score of 1.
- Interactions are capped, multiple actions won’t boost the score beyond 1.
- A first-time view counts as casual browsing (score = 0); a repeat view confirms interest (score = 1).
- The more users that meaningfully engage with a piece of content, the higher it ranks.

### Metadata-driven enhancement

**Basic** **metadata**: Includes things like course type, category, difficulty level, or user attributes like role or location.

**Free** **text metadata**: Draws meaning from course summaries, descriptions, titles, and other long-form text fields to match content with user interests more precisely.

### Models available for the Recommendation engine

We have 3 models available to use for the Recommendation engine with **Full hybrid** being the default:

- **Matrix factorisation** is simple and powerful—but needs a lot of user activity.
- **Partial hybrid** adds flexibility by using extra info—but recommendations may still lack personalisation.
- **Full hybrid** is the most powerful and personal—but also requires the most computational resources to run.

#### Comparing recommender models

| Model | What it uses | Strengths | Limitations | Best for |
| --- | --- | --- | --- | --- |
| **Matrix factorisation** | User behaviour (e.g. ratings and clicks) | Learns patterns, very personalised | Needs lots of user activity; struggles with new users or content | Platforms with lots of rating data |
| **Partial hybrid** | Behaviour + basic full text (e.g. profile and content descriptions) | More accurate, helps with new users | Needs some structured information about users and content. Basic metadata (age, location) can group different users together | When you have some user/item information to work with |
| **Full hybrid** | Behaviour + full text (e.g. profile and content descriptions) | Deep personalisation, works even with minimal behaviour data | Resource hungry and requires clean, rich text | Rich content platforms like courses, news or learning tools |

### Recommendation engine settings

You can configure how the [Recommendation engine](https://portal.document360.io/15/docs/what-are-recommendations) works by navigating to *Quick-access menu > Plugins > Machine learning settings >* *Recommendation e**ngine*.

Note: that the Recommendation engine will not work until you install and configure the engine. Find out how to do this in the [developer documentation](https://help.totaralearning.com/display/DEV/Recommender+installation+and+configuration).

Remember to click **Save changes** when you have finished configuring these settings. The other settings on the **Recommendation engine** page are only used by the legacy recommendation engine, and will be deprecated in a future release.

The **Recommendation algorithm** and **Processing threads** configurations can be modified from their default values by setting environment variables before starting the Machine Learning Service, as described in the README.md file of the Machine Learning Service (integration/extensions/ml_service/README.md).

| **Setting** | **Description** | **Notes** |
| --- | --- | --- |
| Number of items-to-user recommendations | Select the number of items-to-user recommendations generated by the recommendation engine | The default/recommended value is 5 |
| Number of items-to-item recommendations | Select the number of items-to-item recommendations generated by the recommendation engine | The default/recommended value is 5 |
| Time to analyse interactions | Set the time period (in weeks) from which user-item interaction data will be drawn | The default/recommended value is 16 weeks. |

  

Join the [Totara Community](https://totara.community/) for more resources to help you get the most out of Totara.

© Copyright 2026 Totara Learning Solutions. All rights reserved.