# Real-time item recommendations in Amazon Personalize
If your use case or recipe generates item recommendations, after you [create a recommender](creating-recommenders.md) or [create a campaign](campaigns.md), you can get real-time personalized or related item recommendations for your users.
If your domain use case or recipe provides [real-time personalization](use-case-recipe-features.md#about-real-time-personalization), such as the *Top picks for you* use case or the *User-Personalization-v2* recipe, Amazon Personalize updates recommendations based on your user's most recent activity as you record their interactions with your catalog. For more information on recording real-time events and personalization, see [Recording real-time events to influence recommendations](recording-events.md).
When you get real-time item recommendations, you can do the following:
+ If you configured your campaign to return metadata for recommended items, you can specify the columns to include in your [GetRecommendations](API_RS_GetRecommendations.md) API operation. Or you can specify the columns when you test the campaign with the Amazon Personalize console. For code samples, see [Getting item metadata with real-time recommendations](getting-recommendations-with-metadata.md). For information about enabling metadata for a campaign, see [Item metadata in recommendations](campaigns.md#create-campaign-return-metadata). For information about enabling metadata for a recommender, see [Enabling metadata in recommendations for a domain recommender in Amazon Personalize](create-recommender-return-metadata.md).
+ For some use cases and recipes, you can specify a promotion in your recommendation request. A *promotion* defines additional business rules that apply to a configurable subset of recommended items. For more information, see [Promoting items in real-time recommendations](promoting-items.md).
+ You can filter results based on custom criteria. For example, you might not want to recommend products that a user has already purchased or recommend only items for a particular age group. For more information, see [Filtering recommendations and user segments](filter.md).
**Note**
If you used a PERSONALIZED\$1RANKING custom recipe, see [Getting a personalized ranking (custom resources)](rankings.md).
**Topics**
+ [How recommendation scoring works (custom resources)](#how-recommendation-scoring-works)
+ [Recommendation reasons with User-Personalization-v2](#recommendation-reasons)
+ [Getting real-time item recommendations](getting-real-time-item-recommendations.md)
+ [Getting item metadata with real-time recommendations](getting-recommendations-with-metadata.md)
+ [Promoting items in real-time recommendations](promoting-items.md)
## How recommendation scoring works (custom resources)
With the User-Personalization-v2 and User-Personalization recipes, 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.
**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](rankings.md#how-ranking-scoring-works).
Amazon Personalize generates scores for items relative to each other on a scale from 0 to 1 (both inclusive). With User-Personalization-v2, Amazon Personalize generates scores for a subset of your items. With User-Personalization, Amazon Personalize scores all of the items in your catalog.
If you use User-Personalization-v2 and apply a filter to recommendations, depending on how many recommendations the filter removes, Amazon Personalize might add placeholder items. It does this to meet the `numResults` for your recommendation request. These items are popular items, based on amount of interactions data, that satisfy your filter criteria. They don't have a relevance score for the user.
For both User-Personalization-v2 and User-Personalization, the total of all scores equals 1. For example, if you're getting movie recommendations for a user and there are three movies appearing the Items dataset and Interactions dataset, their scores might be `0.6`, `0.3`, and `0.1`. Similarly, if you have 10,000 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 with scores:
![\[Depicts the formula used to calculate scores for each item in recommendations.\]](http://docs.amazonaws.cn/en_us/personalize/latest/dg/images/get_recommendations_score.png)
## Recommendation reasons with User-Personalization-v2
If you use User-Personalization-v2, items the model wouldn't normally recommend include a `reason` list. These reasons explain why the item was included in recommendations. Possible reasons include the following:
+ Promoted item – Indicates the item was included as part of a promotion that you applied in your recommendation request.
+ Exploration – Indicates the item was included with exploration. With exploration, recommendations include items with less interactions data or relevance for the user. For more information about exploration, see [Exploration](https://docs.amazonaws.cn/personalize/latest/dg/use-case-recipe-features.html#about-exploration).
+ Popular item – Indicates the item was included as a placeholder popular item. If you use a filter, depending on how many recommendations the filter removes, Amazon Personalize might add placeholder items to meet the `numResults` for your recommendation request. These items are popular items, based on interactions data, that satisfy your filter criteria. They don't have a relevance score for the user.