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Understand the weighting curve for product recommendations
Understand the weighting curve for product recommendations

To handle situations where very old data skews results and less relevant products are recommended, we apply a weighting curve.

Gareth Burroughes avatar
Written by Gareth Burroughes
Updated over a week ago

Understanding the weighting curve

By default, product recommendations look at data over a period of all time.

The weighting curve’s formula considers data across a timeline, from the date of the first event (such as an order or page view) to the most recent event. Results are then scored between 0.1 and 1 based on their position in the timeline.


The weighting curve strongly favours more recent events. Only items placed in the last 20% of the timeline receive a weighted score above 0.5. From there, the weighting rises rapidly to 1.

Custom weighting curves

The products you sell, customer type, seasonality, and other factors may affect how you think about the recency of your order or Web Behavior Tracking data.

If you would to like try a custom weighting curve, please speak to your Customer Success representative.

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