recombee_api_client.api_requests.recommend_items_to_item module

class recombee_api_client.api_requests.recommend_items_to_item.RecommendItemsToItem(item_id, target_user_id, count, scenario=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), cascade_create=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), return_properties=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), included_properties=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), filter=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), booster=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), logic=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), user_impact=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), diversity=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), min_relevance=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), rotation_rate=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), rotation_time=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), expert_settings=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'), return_ab_group=UUID('59a0cf5d-9a24-4582-be09-92279b50cfc3'))

Bases: recombee_api_client.api_requests.request.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:

It is also possible to use POST HTTP method (for example in case of very long ReQL filter) - query parameters then become body parameters.

Required parameters:

Parameters
  • item_id – ID of the item for which the recommendations are to be generated.

  • target_user_id – 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.

Parameters

count – Number of items 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 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.

  • return_properties – 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:

```

E{lb}

“recommId”: “0c6189e7-dc1a-429a-b613-192696309361”,

“recomms”:

[

E{lb}

“id”: “tv-178”,

“values”: E{lb}

“description”: “4K TV with 3D feature”,

“categories”: [“Electronics”, “Televisions”],

“price”: 342,

“url”: “myshop.com/tv-178” E{rb} E{rb},

E{lb}

“id”: “mixer-42”,

“values”: E{lb}

“description”: “Stainless Steel Mixer”,

“categories”: [“Home & Kitchen”],

“price”: 39,

“url”: “myshop.com/mixer-42” 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=description,price:

```

E{lb}

“recommId”: “6842c725-a79f-4537-a02c-f34d668a3f80”,

“recomms”:

[

E{lb}

“id”: “tv-178”,

“values”: E{lb}

“description”: “4K TV with 3D feature”,

“price”: 342 E{rb} E{rb},

E{lb}

“id”: “mixer-42”,

“values”: E{lb}

“description”: “Stainless Steel Mixer”,

“price”: 39 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
  • user_impactExpert 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.

  • diversityExpert 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.

  • min_relevanceExpert 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.

  • rotation_rateExpert 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.

  • rotation_timeExpert 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.

  • 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()

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

get_query_parameters()

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