Recombee API Client
Public Member Functions | Properties | List of all members
Recombee.ApiClient.ApiRequests.RecommendItemsToUser Class Reference

Recommend items to user More...

Inheritance diagram for Recombee.ApiClient.ApiRequests.RecommendItemsToUser:
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Collaboration diagram for Recombee.ApiClient.ApiRequests.RecommendItemsToUser:
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Public Member Functions

 RecommendItemsToUser (string userId, long count, string scenario=null, bool? cascadeCreate=null, bool? returnProperties=null, string[] includedProperties=null, string filter=null, string booster=null, Logic logic=null, double? diversity=null, string minRelevance=null, double? rotationRate=null, double? rotationTime=null, Dictionary< string, object > expertSettings=null, bool? returnAbGroup=null)
 Construct the request More...
 
override string Path ()
 
Returns
URI to the endpoint including path parameters
More...
 
override Dictionary< string, object > QueryParameters ()
 Get query parameters More...
 
override Dictionary< string, object > BodyParameters ()
 Get body parameters More...
 
- Public Member Functions inherited from Recombee.ApiClient.ApiRequests.Request
 Request (HttpMethod httpMethod, int timeoutMilliseconds, bool ensureHttps=false)
 Construct the request More...
 

Properties

string UserId [get]
 ID of the user for whom personalized recommendations are to be generated. More...
 
long Count [get]
 Number of items to be recommended (N for the top-N recommendation). More...
 
string Scenario [get]
 Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can set various settings to the scenario in the Admin UI. 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. More...
 
bool? CascadeCreate [get]
 If the user does not exist in the database, returns a list of non-personalized recommendations and creates the user in the database. This allows for example rotations in the following recommendations for that user, as the user will be already known to the system. More...
 
bool? ReturnProperties [get]
 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: More...
 
string[] IncludedProperties [get]
 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: More...
 
string Filter [get]
 Boolean-returning ReQL expression which allows you to filter recommended items based on the values of their attributes. Filters can be also assigned to a scenario in the Admin UI. More...
 
string Booster [get]
 Number-returning ReQL 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 in the Admin UI. More...
 
Logic Logic [get]
 Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain and use case. See this section 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 in the Admin UI. More...
 
double? Diversity [get]
 **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. More...
 
string MinRelevance [get]
 **Expert option** 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. More...
 
double? RotationRate [get]
 **Expert option** 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. Default: 0.1. More...
 
double? RotationTime [get]
 **Expert option** 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. Default: 7200.0. More...
 
Dictionary< string, object > ExpertSettings [get]
 Dictionary of custom options. More...
 
bool? ReturnAbGroup [get]
 If there is a custom AB-testing running, return name of group to which the request belongs. More...
 
- Properties inherited from Recombee.ApiClient.ApiRequests.Request
TimeSpan Timeout [get, set]
 Timeout for the request in milliseconds More...
 
bool EnsureHttps [get]
 If true, HTTPS must be chosen over HTTP for this request More...
 
HttpMethod RequestHttpMehod [get]
 Used HTTP method More...
 

Additional Inherited Members

- Protected Member Functions inherited from Recombee.ApiClient.ApiRequests.Request
double ConvertToUnixTimestamp (DateTime date)
 
Returns
Converts DateTime to UNIX timestamp (epoch)
More...
 

Detailed Description

Recommend items to user

Based on user's past interactions (purchases, ratings, etc.) with the items, recommends top-N items that are most likely to be of high value for a given user. The most typical use cases are recommendations at homepage, in some "Picked just for you" section or in email. 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:

Constructor & Destructor Documentation

◆ RecommendItemsToUser()

Recombee.ApiClient.ApiRequests.RecommendItemsToUser.RecommendItemsToUser ( string  userId,
long  count,
string  scenario = null,
bool?  cascadeCreate = null,
bool?  returnProperties = null,
string[]  includedProperties = null,
string  filter = null,
string  booster = null,
Logic  logic = null,
double?  diversity = null,
string  minRelevance = null,
double?  rotationRate = null,
double?  rotationTime = null,
Dictionary< string, object >  expertSettings = null,
bool?  returnAbGroup = null 
)
inline

Construct the request

Parameters
userIdID of the user for whom personalized recommendations are to be generated.
countNumber of items to be recommended (N for the top-N recommendation).
scenarioScenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can set various settings to the scenario in the Admin UI. 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.
cascadeCreateIf the user does not exist in the database, returns a list of non-personalized recommendations and creates the user in the database. This allows for example rotations in the following recommendations for that user, as the user will be already known to the system.
returnPropertiesWith 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": "ce52ada4-e4d9-4885-943c-407db2dee837",
"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
}
Parameters
includedPropertiesAllows 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": "a86ee8d5-cd8e-46d1-886c-8b3771d0520b",
"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
}
Parameters
filterBoolean-returning ReQL expression which allows you to filter recommended items based on the values of their attributes. Filters can be also assigned to a scenario in the Admin UI.
boosterNumber-returning ReQL 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 in the Admin UI.
logicLogic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain and use case. See this section 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 in the Admin UI.
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**Expert option** 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**Expert option** 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. Default: 0.1.
rotationTime**Expert option** 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. Default: 7200.0.
expertSettingsDictionary of custom options.
returnAbGroupIf there is a custom AB-testing running, return name of group to which the request belongs.

Member Function Documentation

◆ BodyParameters()

override Dictionary<string, object> Recombee.ApiClient.ApiRequests.RecommendItemsToUser.BodyParameters ( )
inlinevirtual

Get body parameters

Returns
Dictionary containing values of body parameters (name of parameter: value of the parameter)

Implements Recombee.ApiClient.ApiRequests.Request.

◆ Path()

override string Recombee.ApiClient.ApiRequests.RecommendItemsToUser.Path ( )
inlinevirtual

Returns
URI to the endpoint including path parameters

Implements Recombee.ApiClient.ApiRequests.Request.

◆ QueryParameters()

override Dictionary<string, object> Recombee.ApiClient.ApiRequests.RecommendItemsToUser.QueryParameters ( )
inlinevirtual

Get query parameters

Returns
Dictionary containing values of query parameters (name of parameter: value of the parameter)

Implements Recombee.ApiClient.ApiRequests.Request.

Property Documentation

◆ Booster

string Recombee.ApiClient.ApiRequests.RecommendItemsToUser.Booster
get

Number-returning ReQL 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 in the Admin UI.

◆ CascadeCreate

bool? Recombee.ApiClient.ApiRequests.RecommendItemsToUser.CascadeCreate
get

If the user does not exist in the database, returns a list of non-personalized recommendations and creates the user in the database. This allows for example rotations in the following recommendations for that user, as the user will be already known to the system.

◆ Count

long Recombee.ApiClient.ApiRequests.RecommendItemsToUser.Count
get

Number of items to be recommended (N for the top-N recommendation).

◆ Diversity

double? Recombee.ApiClient.ApiRequests.RecommendItemsToUser.Diversity
get

**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.

◆ ExpertSettings

Dictionary<string, object> Recombee.ApiClient.ApiRequests.RecommendItemsToUser.ExpertSettings
get

Dictionary of custom options.

◆ Filter

string Recombee.ApiClient.ApiRequests.RecommendItemsToUser.Filter
get

Boolean-returning ReQL expression which allows you to filter recommended items based on the values of their attributes. Filters can be also assigned to a scenario in the Admin UI.

◆ IncludedProperties

string [] Recombee.ApiClient.ApiRequests.RecommendItemsToUser.IncludedProperties
get

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": "a86ee8d5-cd8e-46d1-886c-8b3771d0520b",
"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
}

◆ Logic

Logic Recombee.ApiClient.ApiRequests.RecommendItemsToUser.Logic
get

Logic specifies particular behavior of the recommendation models. You can pick tailored logic for your domain and use case. See this section 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 in the Admin UI.

◆ MinRelevance

string Recombee.ApiClient.ApiRequests.RecommendItemsToUser.MinRelevance
get

**Expert option** 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.

◆ ReturnAbGroup

bool? Recombee.ApiClient.ApiRequests.RecommendItemsToUser.ReturnAbGroup
get

If there is a custom AB-testing running, return name of group to which the request belongs.

◆ ReturnProperties

bool? Recombee.ApiClient.ApiRequests.RecommendItemsToUser.ReturnProperties
get

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": "ce52ada4-e4d9-4885-943c-407db2dee837",
"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
}

◆ RotationRate

double? Recombee.ApiClient.ApiRequests.RecommendItemsToUser.RotationRate
get

**Expert option** 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. Default: 0.1.

◆ RotationTime

double? Recombee.ApiClient.ApiRequests.RecommendItemsToUser.RotationTime
get

**Expert option** 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. Default: 7200.0.

◆ Scenario

string Recombee.ApiClient.ApiRequests.RecommendItemsToUser.Scenario
get

Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can set various settings to the scenario in the Admin UI. 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.

◆ UserId

string Recombee.ApiClient.ApiRequests.RecommendItemsToUser.UserId
get

ID of the user for whom personalized recommendations are to be generated.


The documentation for this class was generated from the following file: