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

Recommend users to user More...

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

 RecommendUsersToUser (string userId, long count, string filter=null, string booster=null, bool?cascadeCreate=null, string scenario=null, bool?returnProperties=null, string[] includedProperties=null, double?diversity=null, string minRelevance=null, double?rotationRate=null, double?rotationTime=null, Dictionary< string, object > expertSettings=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]
 User to which we find similar users More...
 
long Count [get]
 Number of users to be recommended (N for the top-N recommendation). More...
 
string Filter [get]
 Boolean-returning ReQL expression which allows you to filter recommended users based on the values of their attributes. More...
 
string Booster [get]
 Number-returning ReQL expression which allows you to boost recommendation rate of some users based on the values of their attributes. 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...
 
string Scenario [get]
 Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the 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 ReturnProperties [get]
 With returnProperties=true, property values of the recommended users are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying the recommended users. Example response:

{
"recommId": "9cb9c55d-50ba-4478-84fd-ab456136156e",
"recomms":
[
{
"id": "user-17",
"values": {
"country": "US",
"sex": "F"
}
},
{
"id": "user-2",
"values": {
"country": "CAN",
"sex": "M"
}
}
]
}
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=country:

{
"recommId": "b326d82d-5d57-4b45-b362-c9d6f0895855",
"recomms":
[
{
"id": "user-17",
"values": {
"country": "US"
}
},
{
"id": "user-2",
"values": {
"country": "CAN"
}
}
]
}
More...
 
double Diversity [get]
 **Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended users 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 users be. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of users equal to count at any cost. If there are not enough data (such as interactions or user 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 users of at least the requested relevancy, and may return less than count users 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 user 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 users. More...
 
double RotationTime [get]
 **Expert option** Taking rotationRate into account, specifies how long time it takes to an user to recover from the penalization. For example, rotationTime=7200.0 means that users recommended less than 2 hours ago are penalized. More...
 
Dictionary< string, object > ExpertSettings [get]
 Dictionary of custom options. 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 users to user

Get similar users as some given user, based on the user's past interactions (purchases, ratings, etc.) and values of properties. It is also possible to use POST HTTP method (for example in case of very long ReQL filter) - query parameters then become body parameters.

Constructor & Destructor Documentation

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

Construct the request

Parameters
userIdUser to which we find similar users
countNumber of users to be recommended (N for the top-N recommendation).
filterBoolean-returning ReQL expression which allows you to filter recommended users based on the values of their attributes.
boosterNumber-returning ReQL expression which allows you to boost recommendation rate of some users based on the values of their attributes.
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.
scenarioScenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the 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.
returnPropertiesWith returnProperties=true, property values of the recommended users are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying the recommended users. Example response:
{
"recommId": "9cb9c55d-50ba-4478-84fd-ab456136156e",
"recomms":
[
{
"id": "user-17",
"values": {
"country": "US",
"sex": "F"
}
},
{
"id": "user-2",
"values": {
"country": "CAN",
"sex": "M"
}
}
]
}
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=country:
{
"recommId": "b326d82d-5d57-4b45-b362-c9d6f0895855",
"recomms":
[
{
"id": "user-17",
"values": {
"country": "US"
}
},
{
"id": "user-2",
"values": {
"country": "CAN"
}
}
]
}
diversity**Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended users 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 users be. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of users equal to count at any cost. If there are not enough data (such as interactions or user 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 users of at least the requested relevancy, and may return less than count users 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 user 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 users.
rotationTime**Expert option** Taking rotationRate into account, specifies how long time it takes to an user to recover from the penalization. For example, rotationTime=7200.0 means that users recommended less than 2 hours ago are penalized.
expertSettingsDictionary of custom options.

Member Function Documentation

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

Get body parameters

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

Implements Recombee.ApiClient.ApiRequests.Request.

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

Returns
URI to the endpoint including path parameters

Implements Recombee.ApiClient.ApiRequests.Request.

override Dictionary<string, object> Recombee.ApiClient.ApiRequests.RecommendUsersToUser.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

string Recombee.ApiClient.ApiRequests.RecommendUsersToUser.Booster
get

Number-returning ReQL expression which allows you to boost recommendation rate of some users based on the values of their attributes.

bool Recombee.ApiClient.ApiRequests.RecommendUsersToUser.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.

long Recombee.ApiClient.ApiRequests.RecommendUsersToUser.Count
get

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

double Recombee.ApiClient.ApiRequests.RecommendUsersToUser.Diversity
get

**Expert option** Real number from [0.0, 1.0] which determines how much mutually dissimilar should the recommended users be. The default value is 0.0, i.e., no diversification. Value 1.0 means maximal diversification.

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

Dictionary of custom options.

string Recombee.ApiClient.ApiRequests.RecommendUsersToUser.Filter
get

Boolean-returning ReQL expression which allows you to filter recommended users based on the values of their attributes.

string [] Recombee.ApiClient.ApiRequests.RecommendUsersToUser.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=country:

{
"recommId": "b326d82d-5d57-4b45-b362-c9d6f0895855",
"recomms":
[
{
"id": "user-17",
"values": {
"country": "US"
}
},
{
"id": "user-2",
"values": {
"country": "CAN"
}
}
]
}

string Recombee.ApiClient.ApiRequests.RecommendUsersToUser.MinRelevance
get

**Expert option** Specifies the threshold of how much relevant must the recommended users be. Possible values one of: "low", "medium", "high". The default value is "low", meaning that the system attempts to recommend number of users equal to count at any cost. If there are not enough data (such as interactions or user 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 users of at least the requested relevancy, and may return less than count users when there is not enough data to fulfill it.

bool Recombee.ApiClient.ApiRequests.RecommendUsersToUser.ReturnProperties
get

With returnProperties=true, property values of the recommended users are returned along with their IDs in a JSON dictionary. The acquired property values can be used for easy displaying the recommended users. Example response:

{
"recommId": "9cb9c55d-50ba-4478-84fd-ab456136156e",
"recomms":
[
{
"id": "user-17",
"values": {
"country": "US",
"sex": "F"
}
},
{
"id": "user-2",
"values": {
"country": "CAN",
"sex": "M"
}
}
]
}

double Recombee.ApiClient.ApiRequests.RecommendUsersToUser.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 user 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 users.

double Recombee.ApiClient.ApiRequests.RecommendUsersToUser.RotationTime
get

**Expert option** Taking rotationRate into account, specifies how long time it takes to an user to recover from the penalization. For example, rotationTime=7200.0 means that users recommended less than 2 hours ago are penalized.

string Recombee.ApiClient.ApiRequests.RecommendUsersToUser.Scenario
get

Scenario defines a particular application of recommendations. It can be for example "homepage", "cart" or "emailing". You can see each scenario in the 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.

string Recombee.ApiClient.ApiRequests.RecommendUsersToUser.UserId
get

User to which we find similar users


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