Amazon Personalize enables popularity tuning for similar item recommendations

Today, Amazon Personalize announces the launch of popularity tuning for its Similar-Items recipe (aws-similar-items). Similar-Items generates recommendations that are similar to the item that a user selects, helping users discover new items in a customer’s catalog based on the behavior of all users and item metadata.

Every customer’s item catalog and the way that users interact with it are unique to their business. When recommending similar items, some customers may want to place more emphasis on popular items because they increase the likelihood of user interaction, while others may want to de-emphasize popular items to surface recommendations that are more similar to the selected item but are widely less known. This launch gives customers more control over the degree to which popularity influences similar-items recommendations, so they can tune the model to meet their particular business needs.

This feature has launched in all 12 regions where Personalize is available. You can tune popularity in the AWS Console, AWS CLI, or AWS SDK.

Amazon Personalize enables you to personalize your website, app, ads, emails, and more, using the same machine learning technology used by Amazon, without requiring any prior machine learning experience. To get started with Amazon Personalize, visit our documentation.