To explore the impact of this new feature in greater detail, let’s review two examples. We specify a value closer to zero to include more popular items, and specify a value closer to 1 to place less emphasis on popularity. ![]() In this post, we show you how to tune popularity for the Similar-Items recipe. This launch gives you more control over the degree to which popularity influences Similar-Items recommendations, so you can tune the model to meet your particular business needs. 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 less widely known. Previously, this capability was only available for SIMS, the other Related_Items recipe within Amazon Personalize.Įvery customer’s item catalog and the way that users interact with it are unique to their business. ![]() Similar-Items generates recommendations that are similar to the item that a user selects, helping users discover new items in your catalog based on the previous behavior of all users and item metadata. Amazon Personalize now enables popularity tuning for its Similar-Items recipe ( aws-similar-items).
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