recombee_api_client.api_requests.recommend_users_to_user module

class recombee_api_client.api_requests.recommend_users_to_user.RecommendUsersToUser(user_id: str, count: int, scenario: str = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), cascade_create: bool = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), return_properties: bool = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), included_properties: list = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), filter: str = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), booster: str = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), logic: Union[str, dict] = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), diversity: float = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), min_relevance: str = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), rotation_rate: float = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), rotation_time: float = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), expert_settings: dict = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'), return_ab_group: bool = UUID('f05f050d-985f-45e2-bf4b-8001878cb8a8'))

Bases: recombee_api_client.api_requests.request.Request

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.

The returned users are sorted by similarity (first user being the most similar).

Required parameters:

Parameters

  • user_id – User to whom we find similar users

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

Optional parameters:

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.

Parameters

  • cascade_create – 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.

  • return_properties – 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:

```

E{lb}

“recommId”: “9cb9c55d-50ba-4478-84fd-ab456136156e”,

“recomms”:

[

E{lb}

“id”: “user-17”,

“values”: E{lb}

“country”: “US”,

“sex”: “F” E{rb} E{rb},

E{lb}

“id”: “user-2”,

“values”: E{lb}

“country”: “CAN”,

“sex”: “M” E{rb} E{rb}

],

“numberNextRecommsCalls”: 0 E{rb}

```

Parameters

included_properties – 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:

```

E{lb}

“recommId”: “b326d82d-5d57-4b45-b362-c9d6f0895855”,

“recomms”:

[

E{lb}

“id”: “user-17”,

“values”: E{lb}

“country”: “US” E{rb} E{rb},

E{lb}

“id”: “user-2”,

“values”: E{lb}

“country”: “CAN” E{rb} E{rb}

],

“numberNextRecommsCalls”: 0 E{rb}

```

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).

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).

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).

Parameters

  • 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.

  • min_relevance – Expert option Specifies the threshold of how much relevant must the recommended users be. Possible values one of: “low”, “medium”, “high”.

  • rotation_rate – 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.

  • rotation_time – 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.

  • expert_settings – Dictionary of custom options.

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

get_body_parameters() → dict

Values of body parameters as a dictionary (name of parameter: value of the parameter).

get_query_parameters() → dict

Values of query parameters as a dictionary (name of parameter: value of the parameter).