AI in learning and development – 10 pitfalls you need to avoid

The frequency and strength of this user bubble vortex depends on how well your machine learning is set up and the depth of your recommendations, as well as the variety of data points taken into consideration. However, there will always inevitably be this kind of drag in any system where recommendations are based on similarity. That's why it's important to build an element of randomisation into your algorithms. For example, occasionally suggest a piece of content which may not be the optimal match - a bit of a left field choice. This gives users the chance to break free from the bubble and engage with a variety of content.