# Choosing a recipe
<a name="working-with-predefined-recipes"></a>

 When you create a custom solution, you specify a recipe and configure training parameters. *Recipes* are Amazon Personalize algorithms that are prepared for specific use cases. Amazon Personalize provides recipes, based on common use cases, for training models. When you create a solution version for the solution, Amazon Personalize trains the models backing the solution version based on the recipe and training configuration. 

Amazon Personalize recipes use the following during training:
+ Predefined attributes of your data
+ Predefined feature transformations
+ Predefined algorithms
+ Initial parameter settings for the algorithms

To optimize your model, you can override many of these parameters when you create a solution. For more information, see [Hyperparameters and HPO](customizing-solution-config-hpo.md).

**Topics**
+ [Amazon Personalize recipe types by use case](#use-cases)
+ [Amazon Personalize recipes](#recipe-categories)
+ [Viewing available Amazon Personalize recipes](#listing-recipes)
+ [User-Personalization-v2 recipe](native-recipe-user-personalization-v2.md)
+ [User-Personalization recipe](native-recipe-new-item-USER_PERSONALIZATION.md)
+ [Trending-Now recipe](native-recipe-trending-now.md)
+ [Popularity-Count recipe](native-recipe-popularity.md)
+ [Personalized-Ranking-v2 recipe](native-recipe-personalized-ranking-v2.md)
+ [Personalized-Ranking recipe](native-recipe-search.md)
+ [Semantic-Similarity recipe](native-recipe-semantic-similarity.md)
+ [Similar-Items recipe](native-recipe-similar-items.md)
+ [SIMS recipe](native-recipe-sims.md)
+ [Next-Best-Action recipe](native-recipe-next-best-action.md)
+ [Item-Affinity recipe](item-affinity-recipe.md)
+ [Item-Attribute-Affinity recipe](item-attribute-affinity-recipe.md)
+ [Legacy HRNN recipes](legacy-user-personalization-recipes.md)

## Amazon Personalize recipe types by use case
<a name="use-cases"></a>

To choose your recipe, first choose your use case from the following and note its corresponding recipe type.
+ Recommending items for users (USER\$1PERSONALIZATION recipes)

  To provide personalized recommendations for your users, train your model with a USER\$1PERSONALIZATION recipe. Personalized recommendations help drive better engagement and conversion.
+ Ranking items for a user (PERSONALIZED\$1RANKING recipes) 

  To personalize the order of curated lists or search results for your users, train your model with a PERSONALIZED\$1RANKING recipe. PERSONALIZED\$1RANKING recipes create a personalized list by re-ranking a collection of input items based on predicted interest level for a given user. Personalized lists improve the customer experience and increase customer loyalty and engagement. 
+  Recommending trending or popular items (POPULAR\$1ITEMS recipes) 

  To recommend trending or popular items use a POPULAR\$1ITEMS recipe. You might use a POPULAR\$1ITEMS if your customers highly value what other users are interacting with. Common uses include recommending viral social media content, breaking news articles, or recent sports videos. 
+  Recommending similar items (RELATED\$1ITEMS recipes)

  To recommend similar items, such as items frequently bought together or movies that other users have also watched, you should use a RELATED\$1ITEMS recipe. Recommending similar items can help your customers discover items and can increase user conversion rate. 
+  Recommending the next best action (PERSONALIZED\$1ACTIONS recipes)

  To recommend the next best action for your users in real time, such as signing up for your loyalty program or applying for a credit card, you should use a PERSONALIZED\$1ACTIONS recipe. Recommending the next best action can increase customer loyalty, generate more revenue, and improve your users' experience.
+  Getting user segments (USER\$1SEGMENTATION recipes)

  To get segments of users based on item input data, such as users who will most likely interact with items with a certain attribute, you should use a USER\$1SEGMENTATION recipe. Getting user segments can help you create advanced marketing campaigns that promote different items to different user segments based on the likelihood that they will take an action. 

## Amazon Personalize recipes
<a name="recipe-categories"></a>

Amazon Personalize provides the following types of recipes. Besides behavioral differences, each type has different requirements for getting recommendations, as shown in the following table.


| Recipe type | Recipes | API | API requirements | 
| --- | --- | --- | --- | 
| USER\$1PERSONALIZATION |  [User-Personalization-v2](native-recipe-user-personalization-v2.md) [User-Personalization](native-recipe-new-item-USER_PERSONALIZATION.md) [HRNN recipe (legacy)](native-recipe-hrnn.md) [HRNN-Metadata recipe (legacy)](native-recipe-hrnn-metadata.md) [HRNN-Coldstart recipe (legacy)](native-recipe-hrnn-coldstart.md)  | [GetRecommendations](API_RS_GetRecommendations.md) |  `userId`: Required `itemId`: Not used `inputList`: NA  | 
| POPULAR\$1ITEMS |  [Trending-Now](native-recipe-trending-now.md) [Popularity-Count](native-recipe-popularity.md)  | [GetRecommendations](API_RS_GetRecommendations.md) |  `userId`: Required only if you apply a filter that requires it `itemId`: Not used `inputList`: NA  | 
| PERSONALIZED\$1RANKING |  [Personalized-Ranking-v2](native-recipe-personalized-ranking-v2.md) [Personalized-Ranking](native-recipe-search.md)  | [GetPersonalizedRanking](API_RS_GetPersonalizedRanking.md) |  `userId`: Required `itemId`: NA `inputList`: list of itemId's  | 
| RELATED\$1ITEMS |  [Similar-Items](native-recipe-similar-items.md) [SIMS](native-recipe-sims.md) [Semantic-Similarity recipe](native-recipe-semantic-similarity.md)  | [GetRecommendations](API_RS_GetRecommendations.md) |  `userId`: Required only if you apply a filter that requires it `itemId`: Required `inputList`: NA  | 
| PERSONALIZED\$1ACTIONS |  [Next-Best-Action](native-recipe-next-best-action.md)  | [GetActionRecommendations](API_RS_GetActionRecommendations.md) |  `userId`: Required `actionId`: Not used `itemId`: Not used `inputList`: NA  | 
| USER\$1SEGMENTATION |  [Item-Affinity](item-affinity-recipe.md) [Item-Attribute-Affinity](item-attribute-affinity-recipe.md)  | [CreateBatchSegmentJob](API_CreateBatchSegmentJob.md) |  For batch workflow requirements, see [Getting user segments with a batch segment job](creating-batch-seg-job.md).  | 

## Viewing available Amazon Personalize recipes
<a name="listing-recipes"></a>

To see a list of available recipes:
+ In the Amazon Personalize console, choose a dataset group. From the navigation pane, choose **Solutions and recipes**, and choose the **Recipes** tab. 
+ With the Amazon SDK for Python (Boto3), call the [ListRecipes](API_ListRecipes.md) API. 
+ With the Amazon CLI, use the following command.

  ```
  aws personalize list-recipes
  ```

To get information about a recipe using the SDK for Python (Boto3), call the [DescribeRecipe](API_DescribeRecipe.md) API. To get information about a recipe using the Amazon CLI, use the following command.

```
aws personalize describe-recipe --recipe-arn recipe_arn
```