Getting item recommendations
You can get item recommendations from a Amazon Personalize recommender or custom campaign with the Amazon Personalize console, AWS Command Line Interface (AWS CLI), or AWS SDKs.
Note
If you used a PERSONALIZED_RANKING custom recipe, see Getting a personalized ranking (custom resources).
Topics
How User-Personalization recommendation scoring works (custom resources)
With the User-Personalization recipe, Amazon Personalize generates scores for items based on on a user's interaction data and metadata. These scores represent the relative certainty that Amazon Personalize has in whether the user will interact with the item next. Higher scores represent greater certainty.
Amazon Personalize scores all of the items in your catalog relative to each other on a scale from 0 to 1 (both inclusive), so that the
total of all scores equals 1. For example, if you're getting movie recommendations for a
user and there are three movies in the Items dataset, their scores might be
0.6
, 0.3
, and 0.1
. Similarly, if you have 1,000
movies in your inventory, the highest-scoring movies might have very small scores (the
average score would be.001
), but, because scoring is relative, the
recommendations are still valid.
In mathematical terms, scores for each user-item pair (u,i) are computed according to
the following formula, where exp
is the exponential function,
w̅u and wi/j are user
and item embeddings respectively, and the Greek letter sigma (Σ) represents summation over
all items in the item dataset:
Note
Amazon Personalize doesn't show scores for domain recommenders or the Similar-Items, SIMS or Popularity-Count recipes. For information on scores for Personalized-Ranking recommendations, see How personalized ranking scoring works.