RecommendItemsToItem

java.lang.Object
- com.recombee.api_client.api_requests.Request
- - com.recombee.api_client.api_requests.RecommendItemsToItem

```
public class RecommendItemsToItem
extends Request
```
Recommends set of items that are somehow related to one given item, *X*. Typical scenario is when user *A* is viewing *X*. Then you may display items to the user that he might be also interested in. Recommend items to item request gives you Top-N such items, optionally taking the target user *A* into account. The returned items are sorted by relevance (first item being the most relevant). Besides the recommended items, also a unique `recommId` is returned in the response. It can be used to: - Let Recombee know that this recommendation was successful (e.g. user clicked one of the recommended items). See [Reported metrics](https://docs.recombee.com/admin_ui.html#reported-metrics). - Get subsequent recommended items when the user scrolls down (*infinite scroll*) or goes to the next page. See [Recommend Next Items](https://docs.recombee.com/api.html#recommend-next-items). It is also possible to use POST HTTP method (for example in case of very long ReQL filter) - query parameters then become body parameters.

Field Summary

Fields
Modifier and Type	Field and Description
`protected java.lang.String`	`booster` Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes.
`protected java.lang.Boolean`	`cascadeCreate` If item of given itemId or user of given targetUserId doesn't exist in the database, it creates the missing entity/entities and returns some (non-personalized) recommendations.
`protected java.lang.Long`	`count` Number of items to be recommended (N for the top-N recommendation).
`protected java.lang.Double`	`diversity` Expert option Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be.
`protected java.util.Map<java.lang.String,java.lang.Object>`	`expertSettings` Dictionary of custom options.
`protected java.lang.String`	`filter` Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes.
`protected java.lang.String[]`	`includedProperties` Allows to specify, which properties should be returned when `returnProperties=true` is set.
`protected java.lang.String`	`itemId` ID of the item for which the recommendations are to be generated.
`protected Logic`	`logic` Logic specifies particular behavior of the recommendation models.
`protected java.lang.String`	`minRelevance` Expert option If the targetUserId is provided: Specifies the threshold of how much relevant must the recommended items be to the user.
`protected java.lang.Boolean`	`returnAbGroup` If there is a custom AB-testing running, return name of group to which the request belongs.
`protected java.lang.Boolean`	`returnProperties` With `returnProperties=true`, property values of the recommended items are returned along with their IDs in a JSON dictionary.
`protected java.lang.Double`	`rotationRate` Expert option If the targetUserId is provided: If your users browse the system in real-time, it may easily happen that you wish to offer them recommendations multiple times.
`protected java.lang.Double`	`rotationTime` Expert option If the targetUserId is provided: Taking rotationRate into account, specifies how long time it takes to an item to recover from the penalization.
`protected java.lang.String`	`scenario` Scenario defines a particular application of recommendations.
`protected java.lang.String`	`targetUserId` ID of the user who will see the recommendations.
`protected java.lang.Double`	`userImpact` Expert option If targetUserId parameter is present, the recommendations are biased towards the given user.

Fields inherited from class com.recombee.api_client.api_requests.Request
timeout

Constructor Summary

Constructors
Constructor and Description
`RecommendItemsToItem(java.lang.String itemId, java.lang.String targetUserId, long count)` Construct the request

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`java.util.Map<java.lang.String,java.lang.Object>`	`getBodyParameters()` Get body parameters
`java.lang.String`	`getBooster()`
`boolean`	`getCascadeCreate()`
`long`	`getCount()`
`double`	`getDiversity()`
`java.util.Map<java.lang.String,java.lang.Object>`	`getExpertSettings()`
`java.lang.String`	`getFilter()`
`HTTPMethod`	`getHTTPMethod()`
`java.lang.String[]`	`getIncludedProperties()`
`java.lang.String`	`getItemId()`
`Logic`	`getLogic()`
`java.lang.String`	`getMinRelevance()`
`java.lang.String`	`getPath()`
`java.util.Map<java.lang.String,java.lang.Object>`	`getQueryParameters()` Get query parameters
`boolean`	`getReturnAbGroup()`
`boolean`	`getReturnProperties()`
`double`	`getRotationRate()`
`double`	`getRotationTime()`
`java.lang.String`	`getScenario()`
`java.lang.String`	`getTargetUserId()`
`double`	`getUserImpact()`
`RecommendItemsToItem`	`setBooster(java.lang.String booster)`
`RecommendItemsToItem`	`setCascadeCreate(boolean cascadeCreate)`
`RecommendItemsToItem`	`setDiversity(double diversity)`
`RecommendItemsToItem`	`setExpertSettings(java.util.Map<java.lang.String,java.lang.Object> expertSettings)`
`RecommendItemsToItem`	`setFilter(java.lang.String filter)`
`RecommendItemsToItem`	`setIncludedProperties(java.lang.String[] includedProperties)`
`RecommendItemsToItem`	`setLogic(Logic logic)`
`RecommendItemsToItem`	`setMinRelevance(java.lang.String minRelevance)`
`RecommendItemsToItem`	`setReturnAbGroup(boolean returnAbGroup)`
`RecommendItemsToItem`	`setReturnProperties(boolean returnProperties)`
`RecommendItemsToItem`	`setRotationRate(double rotationRate)`
`RecommendItemsToItem`	`setRotationTime(double rotationTime)`
`RecommendItemsToItem`	`setScenario(java.lang.String scenario)`
`RecommendItemsToItem`	`setUserImpact(double userImpact)`

Methods inherited from class com.recombee.api_client.api_requests.Request
getEnsureHttps, getTimeout, setTimeout

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - itemId
```
protected java.lang.String itemId
```
    ID of the item for which the recommendations are to be generated.
  - targetUserId
```
protected java.lang.String targetUserId
```
    ID of the user who will see the recommendations. Specifying the *targetUserId* is beneficial because: * It makes the recommendations personalized * Allows the calculation of Actions and Conversions in the graphical user interface, as Recombee can pair the user who got recommendations and who afterwards viewed/purchased an item. If you insist on not specifying the user, pass `null` (`None`, `nil`, `NULL` etc. depending on language) to *targetUserId*. Do not create some special dummy user for getting recommendations, as it could mislead the recommendation models, and result in wrong recommendations. For anonymous/unregistered users it is possible to use for example their session ID.
  - count
```
protected java.lang.Long count
```
    Number of items to be recommended (N for the top-N recommendation).
  - scenario
```
protected java.lang.String scenario
```
    Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can set various settings to the [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com). You can also see performance of each scenario in the Admin UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
  - cascadeCreate
```
protected java.lang.Boolean cascadeCreate
```
    If item of given *itemId* or user of given *targetUserId* doesn't exist in the database, it creates the missing entity/entities and returns some (non-personalized) recommendations. This allows for example rotations in the following recommendations for the user of given *targetUserId*, as the user will be already known to the system.
  - returnProperties
```
protected java.lang.Boolean returnProperties
```
    With `returnProperties=true`, property values of the recommended items are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying of the recommended items to the user. Example response: ``` { "recommId": "0c6189e7-dc1a-429a-b613-192696309361", "recomms": [ { "id": "tv-178", "values": { "description": "4K TV with 3D feature", "categories": ["Electronics", "Televisions"], "price": 342, "url": "myshop.com/tv-178" } }, { "id": "mixer-42", "values": { "description": "Stainless Steel Mixer", "categories": ["Home & Kitchen"], "price": 39, "url": "myshop.com/mixer-42" } } ], "numberNextRecommsCalls": 0 } ```
  - includedProperties
```
protected java.lang.String[] includedProperties
```
    Allows to specify, which properties should be returned when `returnProperties=true` is set. The properties are given as a comma-separated list. Example response for `includedProperties=description,price`: ``` { "recommId": "6842c725-a79f-4537-a02c-f34d668a3f80", "recomms": [ { "id": "tv-178", "values": { "description": "4K TV with 3D feature", "price": 342 } }, { "id": "mixer-42", "values": { "description": "Stainless Steel Mixer", "price": 39 } } ], "numberNextRecommsCalls": 0 } ```
  - filter
```
protected java.lang.String filter
```
    Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes. Filters can be also assigned to a [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com).
  - booster
```
protected java.lang.String booster
```
    Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes. Boosters can be also assigned to a [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com).
  - logic
```
protected Logic logic
```
    Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain and use case. See [this section](https://docs.recombee.com/recommendation_logics.html) for list of available logics and other details. The difference between `logic` and `scenario` is that `logic` specifies mainly behavior, while `scenario` specifies the place where recommendations are shown to the users. Logic can be also set to a [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com).
  - userImpact
```
protected java.lang.Double userImpact
```
    **Expert option** If *targetUserId* parameter is present, the recommendations are biased towards the given user. Using *userImpact*, you may control this bias. For an extreme case of `userImpact=0.0`, the interactions made by the user are not taken into account at all (with the exception of history-based blacklisting), for `userImpact=1.0`, you'll get user-based recommendation. The default value is `0`.
  - diversity
```
protected java.lang.Double diversity
```
    **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.
  - minRelevance
```
protected java.lang.String minRelevance
```
    **Expert option** If the *targetUserId* is provided: Specifies the threshold of how much relevant must the recommended items be to the user. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of items equal to *count* at any cost. If there are not enough data (such as interactions or item properties), this may even lead to bestseller-based recommendations to be appended to reach the full *count*. This behavior may be suppressed by using "medium" or "high" values. In such case, the system only recommends items of at least the requested relevance, and may return less than *count* items when there is not enough data to fulfill it.
  - rotationRate
```
protected java.lang.Double rotationRate
```
    **Expert option** If the *targetUserId* is provided: If your users browse the system in real-time, it may easily happen that you wish to offer them recommendations multiple times. Here comes the question: how much should the recommendations change? Should they remain the same, or should they rotate? Recombee API allows you to control this per-request in backward fashion. You may penalize an item for being recommended in the near past. For the specific user, `rotationRate=1` means maximal rotation, `rotationRate=0` means absolutely no rotation. You may also use, for example `rotationRate=0.2` for only slight rotation of recommended items.
  - rotationTime
```
protected java.lang.Double rotationTime
```
    **Expert option** If the *targetUserId* is provided: Taking *rotationRate* into account, specifies how long time it takes to an item to recover from the penalization. For example, `rotationTime=7200.0` means that items recommended less than 2 hours ago are penalized.
  - expertSettings
```
protected java.util.Map<java.lang.String,java.lang.Object> expertSettings
```
    Dictionary of custom options.
  - returnAbGroup
```
protected java.lang.Boolean returnAbGroup
```
    If there is a custom AB-testing running, return name of group to which the request belongs.
- Constructor Detail
  - RecommendItemsToItem
```
public RecommendItemsToItem(java.lang.String itemId,
                            java.lang.String targetUserId,
                            long count)
```
    Construct the request
    
    Parameters:
    
    itemId - ID of the item for which the recommendations are to be generated.
    
    targetUserId - ID of the user who will see the recommendations. Specifying the *targetUserId* is beneficial because: * It makes the recommendations personalized * Allows the calculation of Actions and Conversions in the graphical user interface, as Recombee can pair the user who got recommendations and who afterwards viewed/purchased an item. If you insist on not specifying the user, pass `null` (`None`, `nil`, `NULL` etc. depending on language) to *targetUserId*. Do not create some special dummy user for getting recommendations, as it could mislead the recommendation models, and result in wrong recommendations. For anonymous/unregistered users it is possible to use for example their session ID.
    
    count - Number of items to be recommended (N for the top-N recommendation).
- Method Detail
  - setScenario
```
public RecommendItemsToItem setScenario(java.lang.String scenario)
```
    Parameters:
    
    scenario - Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can set various settings to the [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com). You can also see performance of each scenario in the Admin UI separately, so you can check how well each application performs. The AI which optimizes models in order to get the best results may optimize different scenarios separately, or even use different models in each of the scenarios.
  - setCascadeCreate
```
public RecommendItemsToItem setCascadeCreate(boolean cascadeCreate)
```
    Parameters:
    
    cascadeCreate - If item of given *itemId* or user of given *targetUserId* doesn't exist in the database, it creates the missing entity/entities and returns some (non-personalized) recommendations. This allows for example rotations in the following recommendations for the user of given *targetUserId*, as the user will be already known to the system.
  - setReturnProperties
```
public RecommendItemsToItem setReturnProperties(boolean returnProperties)
```
    Parameters:
    
    returnProperties - With `returnProperties=true`, property values of the recommended items are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying of the recommended items to the user. Example response: ``` { "recommId": "0c6189e7-dc1a-429a-b613-192696309361", "recomms": [ { "id": "tv-178", "values": { "description": "4K TV with 3D feature", "categories": ["Electronics", "Televisions"], "price": 342, "url": "myshop.com/tv-178" } }, { "id": "mixer-42", "values": { "description": "Stainless Steel Mixer", "categories": ["Home & Kitchen"], "price": 39, "url": "myshop.com/mixer-42" } } ], "numberNextRecommsCalls": 0 } ```
  - setIncludedProperties
```
public RecommendItemsToItem setIncludedProperties(java.lang.String[] includedProperties)
```
    Parameters:
    
    includedProperties - Allows to specify, which properties should be returned when `returnProperties=true` is set. The properties are given as a comma-separated list. Example response for `includedProperties=description,price`: ``` { "recommId": "6842c725-a79f-4537-a02c-f34d668a3f80", "recomms": [ { "id": "tv-178", "values": { "description": "4K TV with 3D feature", "price": 342 } }, { "id": "mixer-42", "values": { "description": "Stainless Steel Mixer", "price": 39 } } ], "numberNextRecommsCalls": 0 } ```
  - setFilter
```
public RecommendItemsToItem setFilter(java.lang.String filter)
```
    Parameters:
    
    filter - Boolean-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to filter recommended items based on the values of their attributes. Filters can be also assigned to a [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com).
  - setBooster
```
public RecommendItemsToItem setBooster(java.lang.String booster)
```
    Parameters:
    
    booster - Number-returning [ReQL](https://docs.recombee.com/reql.html) expression which allows you to boost recommendation rate of some items based on the values of their attributes. Boosters can be also assigned to a [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com).
  - setLogic
```
public RecommendItemsToItem setLogic(Logic logic)
```
    Parameters:
    
    logic - Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain and use case. See [this section](https://docs.recombee.com/recommendation_logics.html) for list of available logics and other details. The difference between `logic` and `scenario` is that `logic` specifies mainly behavior, while `scenario` specifies the place where recommendations are shown to the users. Logic can be also set to a [scenario](https://docs.recombee.com/scenarios.html) in the [Admin UI](https://admin.recombee.com).
  - setUserImpact
```
public RecommendItemsToItem setUserImpact(double userImpact)
```
    Parameters:
    
    userImpact - **Expert option** If *targetUserId* parameter is present, the recommendations are biased towards the given user. Using *userImpact*, you may control this bias. For an extreme case of `userImpact=0.0`, the interactions made by the user are not taken into account at all (with the exception of history-based blacklisting), for `userImpact=1.0`, you'll get user-based recommendation. The default value is `0`.
  - setDiversity
```
public RecommendItemsToItem setDiversity(double diversity)
```
    Parameters:
    
    diversity - **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended items be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.
  - setMinRelevance
```
public RecommendItemsToItem setMinRelevance(java.lang.String minRelevance)
```
    Parameters:
    
    minRelevance - **Expert option** If the *targetUserId* is provided: Specifies the threshold of how much relevant must the recommended items be to the user. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of items equal to *count* at any cost. If there are not enough data (such as interactions or item properties), this may even lead to bestseller-based recommendations to be appended to reach the full *count*. This behavior may be suppressed by using "medium" or "high" values. In such case, the system only recommends items of at least the requested relevance, and may return less than *count* items when there is not enough data to fulfill it.
  - setRotationRate
```
public RecommendItemsToItem setRotationRate(double rotationRate)
```
    Parameters:
    
    rotationRate - **Expert option** If the *targetUserId* is provided: If your users browse the system in real-time, it may easily happen that you wish to offer them recommendations multiple times. Here comes the question: how much should the recommendations change? Should they remain the same, or should they rotate? Recombee API allows you to control this per-request in backward fashion. You may penalize an item for being recommended in the near past. For the specific user, `rotationRate=1` means maximal rotation, `rotationRate=0` means absolutely no rotation. You may also use, for example `rotationRate=0.2` for only slight rotation of recommended items.
  - setRotationTime
```
public RecommendItemsToItem setRotationTime(double rotationTime)
```
    Parameters:
    
    rotationTime - **Expert option** If the *targetUserId* is provided: Taking *rotationRate* into account, specifies how long time it takes to an item to recover from the penalization. For example, `rotationTime=7200.0` means that items recommended less than 2 hours ago are penalized.
  - setExpertSettings
```
public RecommendItemsToItem setExpertSettings(java.util.Map<java.lang.String,java.lang.Object> expertSettings)
```
    Parameters:
    
    expertSettings - Dictionary of custom options.
  - setReturnAbGroup
```
public RecommendItemsToItem setReturnAbGroup(boolean returnAbGroup)
```
    Parameters:
    
    returnAbGroup - If there is a custom AB-testing running, return name of group to which the request belongs.
  - getItemId
```
public java.lang.String getItemId()
```
  - getTargetUserId
```
public java.lang.String getTargetUserId()
```
  - getCount
```
public long getCount()
```
  - getScenario
```
public java.lang.String getScenario()
```
  - getCascadeCreate
```
public boolean getCascadeCreate()
```
  - getReturnProperties
```
public boolean getReturnProperties()
```
  - getIncludedProperties
```
public java.lang.String[] getIncludedProperties()
```
  - getFilter
```
public java.lang.String getFilter()
```
  - getBooster
```
public java.lang.String getBooster()
```
  - getLogic
```
public Logic getLogic()
```
  - getUserImpact
```
public double getUserImpact()
```
  - getDiversity
```
public double getDiversity()
```
  - getMinRelevance
```
public java.lang.String getMinRelevance()
```
  - getRotationRate
```
public double getRotationRate()
```
  - getRotationTime
```
public double getRotationTime()
```
  - getExpertSettings
```
public java.util.Map<java.lang.String,java.lang.Object> getExpertSettings()
```
  - getReturnAbGroup
```
public boolean getReturnAbGroup()
```
  - getHTTPMethod
```
public HTTPMethod getHTTPMethod()
```
    Specified by:
    
    getHTTPMethod in class Request
    
    Returns:
    
    Used HTTP method
  - getPath
```
public java.lang.String getPath()
```
    Specified by:
    
    getPath in class Request
    
    Returns:
    
    URI to the endpoint including path parameters
  - getQueryParameters
```
public java.util.Map<java.lang.String,java.lang.Object> getQueryParameters()
```
    Get query parameters
    
    Specified by:
    
    getQueryParameters in class Request
    
    Returns:
    
    Values of query parameters (name of parameter: value of the parameter)
  - getBodyParameters
```
public java.util.Map<java.lang.String,java.lang.Object> getBodyParameters()
```
    Get body parameters
    
    Specified by:
    
    getBodyParameters in class Request
    
    Returns:
    
    Values of body parameters (name of parameter: value of the parameter)

Class RecommendItemsToItem

Field Summary

Fields inherited from class com.recombee.api_client.api_requests.Request

Constructor Summary

Method Summary

Methods inherited from class com.recombee.api_client.api_requests.Request

Methods inherited from class java.lang.Object

Field Detail

itemId

targetUserId

count

scenario

cascadeCreate

returnProperties

includedProperties

filter

booster

logic

userImpact

diversity

minRelevance

rotationRate

rotationTime

expertSettings

returnAbGroup

Constructor Detail

RecommendItemsToItem

Method Detail

setScenario

setCascadeCreate

setReturnProperties

setIncludedProperties

setFilter

setBooster

setLogic

setUserImpact

setDiversity

setMinRelevance

setRotationRate

setRotationTime

setExpertSettings

setReturnAbGroup

getItemId

getTargetUserId

getCount

getScenario

getCascadeCreate

getReturnProperties

getIncludedProperties

getFilter

getBooster

getLogic

getUserImpact

getDiversity

getMinRelevance

getRotationRate

getRotationTime

getExpertSettings

getReturnAbGroup

getHTTPMethod

getPath

getQueryParameters

getBodyParameters