Recommendations

Recommend Items to Item Segment

Recommends Items that are the most relevant to a particular Segment from a context Segmentation.

Based on the used Segmentation, this endpoint can be used for example for:

Recommending articles related to a particular topic
Recommending songs belonging to a particular genre
Recommending products produced by a particular brand

You need to set the used context Segmentation in the Admin UI in the Scenario settings prior to using this endpoint.

The returned items are sorted by relevance (the first item being the most relevant).

It is also possible to use GET HTTP method - body parameters then become query parameters.

post/{databaseId}/recomms/item-segments/items/

Request

Base URL
http://Based on the region of your database (see https://docs.recombee.com/regions).
URL
http://Based on the region of your database (see https://docs.recombee.com/regions)./{databaseId}/recomms/item-segments/items/
Auth
none declared

Path parameters

databaseIdstring required

ID of your database.

Request body

contextSegmentIdstring required

ID of the segment from contextSegmentationId for which the recommendations are to be generated.

targetUserIdstring required

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 afterward viewed/purchased an item.

If you insist on not specifying the user, pass null (None, nil, NULL etc., depending on the 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.

countinteger required

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

scenariostring

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 the performance of each scenario in the Admin UI separately, so you can check how well each application performs.

The AI that optimizes models to get the best results may optimize different scenarios separately or even use different models in each of the scenarios.

cascadeCreateboolean

If a user of the given targetUserId doesn't exist in the database, it creates this user and returns some (non-personalized) recommendations. This allows, for example, rotations in the following recommendations for the user of the given targetUserId, as the user will be already known to the system.

returnPropertiesboolean

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 to easily display 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
  }

includedPropertiesstring[]

Allows specifying 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
  }

filterstring

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

Filters can also be assigned to a scenario in the Admin UI.

boosterstring

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

Boosters can also be assigned to a scenario in the Admin UI.

reqlExpressionsobject

A dictionary of ReQL expressions that will be executed for each recommended item. This can be used to compute additional properties of the recommended items that are not stored in the database.

The keys are the names of the expressions, and the values are the actual ReQL expressions.

Example request:

{
  "reqlExpressions": {
    "isInUsersCity": "context_user[\"city\"] in 'cities'",
    "distanceToUser": "earth_distance('location', context_user[\"location\"])"
  }
}

Example response:

{
  "recommId": "ce52ada4-e4d9-4885-943c-407db2dee837",
  "recomms": 
    [
      {
        "id": "restaurant-178",
        "reqlEvaluations": {
          "isInUsersCity": true,
          "distanceToUser": 5200.2
        }
      },
      {
        "id": "bar-42",
        "reqlEvaluations": {
          "isInUsersCity": false,
          "distanceToUser": 2516.0
        }
      }
    ],
   "numberNextRecommsCalls": 0
}

minRelevance'low' | 'medium' | 'high'

Expert option: If the targetUserId is provided: Specifies the threshold of how 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 a number of items equal to count at any cost. If there is not enough data (such as interactions or item properties), this may even lead to bestseller-based recommendations being 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.

rotationRatenumber double

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 a 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.

rotationTimenumber double

Expert option: If the targetUserId is provided: Taking rotationRate into account, specifies how long it takes for an item to recover from the penalization. For example, rotationTime=7200.0 means that items recommended less than 2 hours ago are penalized.

expertSettingsobject

Dictionary of custom options.

returnAbGroupboolean

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

Response

successful operation

recommIdstring required

Id of the recommendation request

numberNextRecommsCallsinteger

How many times Recommend Next Items have been called for this recommId

abGroupstring

Name of AB-testing group to which the request belongs if there is a custom AB-testing running.

Changes

No recorded changes to this operation across all 3 revisions of this API.