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Recommendation Logics

Logic allows you to specify the desired behavior of the recommendation model.

While Recombee is generally a domain-independent recommendation engine and the default model is optimized to work well in most cases, we have used our experience in some frequent domains to setup some tuned models, and you can now easily pick the logic that is tailored for the particular case.

Setting logic in the Admin UI#

You can assign a particular logic to a Scenario in Admin UI.

Setting Logic in the Admin UI

Alternatively you can use API parameter to set the logic.

List of Logics#

There is a list logics available for most of the databases. Some databases tweaked by the Recombee support team may have custom logic.

tip20
Tip

See the Integration Tips if you are not sure which logic should be used in your use case.

Universal Models

Universal Recombee models for the most common use-cases.

recombee:default#

Default model ensemble aiming to work well in most common use cases. It is an ensemble of collaborative filtering and content-based recommendation, backed by popularity-based models when there's not enough data in the given context. The ensemble is being continuously optimized by AI so it automatically adapts to incoming data.


recombee:homepage#

Universal model ensemble suitable for putting recommendations on the homepage / welcome-screen scenarios.


Applicable to endpoints

recombee:personal#

Universal model ensemble suitable for user-based recommendations reflecting the personal tastes of the current user.


Applicable to endpoints

Requirements
There must be interaction data for the user.

recombee:similar#

Universal model ensemble suitable for either recommending items which are similar to the given item (in case of Items-to-Item recommendation), or recommending users who are similar to the given user (in case of Users-to-User recommendation).


Universal model suitable for recommending globally popular items, taking into account all interaction types. More important actions (purchases, cart-additions) have higher weight for computing the popularity than less important actions (detail-views).



Requirements
Interaction data (Detail Views/Purchases/Cart Additions) must be present.

Parameters

recombee:recently-viewed#

Model returning the list of recently viewed items for a particular user.


Applicable to endpoints

Requirements
Detail Views must exist for the user.

Parameters
maxAge#
double
Default: 1209600

Maximal age of the DetailView in seconds since the current timestamp.


recencyOrdering#
enum
Default: descending

Order in which the items are returned with regard to the time when they were interacted. When the order is descending, the last interacted item is at the beginning of the list of recommendations. When it is ascending, the first interacted item matching the maxAge condition is at the beginning of the list.

recombee:visually-similar#

Model recommending similar items based on visual similarity (using deep-learning neural networks to process the images provided through the image properties). While the image similarity can take part in other logics as well, in recombee:visually-similar, it is explicitly used as the primary model. Image processing may take some time. Therefore, changes in image item properties do not take effect in real-time. The functionality is still in beta mode.


Applicable to endpoints

Requirements
Some image properties must be defined for items, with valid image links provided for the majority of the items in the catalog.

Parameters
useFallbackModels#
boolean
Default: false

Enable fallback models to make the recommendations if the model based on visual similarity cannot be used (e.g. due to a missing image for the given source item).

recombee:similar-properties#

Model recommending similar items based on item properties (numerical values, sets, and texts). While property values can take part in other logics, in recombee:similar-properties, they are explicitly used as the primary data source for recommending similar items or uses. Various machine-learning methods, including NLP deep-learning networks that process unstructured text descriptions, are used to compute the similarities.



Requirements
Some properties must exist for the items/users, depending on the used API endpoint.

Parameters
useFallbackModels#
boolean
Default: false

Enable fallback models to make recommendations if the model based on item properties cannot be used (e.g. due to missing property values for the given source item).

recombee:emailing#

Model ensemble suitable for recommending items in periodic e-mailing campaigns. It contains fine-tuned rotation settings so that the set of recommended items for a particular user changes with each e-mail sent. By default, rotation only takes into account recommendations (e-mails) at most 14 days old. Should a longer time period be needed for your use case, kindly contact support@recombee.com.


Applicable to endpoints

Parameters
rotationPolicy#
enum
Default: smart

A policy determining how strictly should already recommended items be rotated. The default option "smart" contains fine-tuned rotation settings, which take into account the position of recommended items and enable some of the items to re-appear in further e-mails. Option "total" never recommends items that have already appeared in previous e-mails.

recombee:custom-sort#

Model suitable for recommending items ordered by a ReQL expression given as a booster


Models for both personalized and non-personalized fulltext search.

search:personalized#

Smart personalized fulltext search which takes into account both the search query and the personal preferences of the user.



Parameters
personalizationImpact#
enum
Default: medium

Determines whether personalization or full-text should have a higher priority in the ranking of the items.

search:non-personalized#

Non-personalized fulltext search, based purely on how well the search query is matched.


E-commerce

Models specifically tuned for E-Commerce use-cases.

ecommerce:homepage#

Model ensemble suitable for recommending products on homepage/welcome page in E-Commerce and online retail systems.


Applicable to endpoints

ecommerce:similar-products#

Model ensemble suitable for recommending similar products (alternatives) to a source (currently viewed) product.


Applicable to endpoints

ecommerce:cross-sell#

Model ensemble suitable for recommending compatible/complementary products. In user-based case, the products are compatible with those that the user has already put to the shopping cart or purchased. In item-based case, they are compatible with the source (currently viewed) item.



Requirements
Purchases must be present for the item/user.

ecommerce:bestseller#

Model suitable for recommending globally most purchased items.



Requirements
Purchases must be present in the database.

ecommerce:similarly-purchasing#

Recommends users with similar purchasing behavior as the source user.


Applicable to endpoints

Requirements
Purchases must be present for the user.

Video

Set of fine-tuned Video logics, specifically designed for VOD and similar use cases.

video:continue-watching#

Model ensemble suitable for recommending partially watched movies or episodes, or the next episode from a watched series.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters
watchingStartedPercentage#
int
Default: 10

Percentage of an asset length for it to be considered as being watched.


watchingCompletedPercentage#
int
Default: 90

Percentage of an asset length for it to be considered as completely watched.


assetType#
enum
Default: movies+episodes

Asset type to be recommended.

video:personal#

Model ensemble suitable for recommending personalized rows on the homepage or browse pages. Rows with specific content (e.g. award-winning movies) can be set up by applying filters.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters
assetType#
enum
Default: movies+episodes

Asset type to be recommended.


excludeAlreadyWatched#
boolean
Default: true

Exclude already watched assets.

video:segments-for-you#

Model ensemble suitable for recommending favorite segments (genres, actors, ...) for a specific user. A particular segmentation must exist beforehand.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Model ensemble suitable for recommending globally popular items.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters

Model ensemble suitable for recommendations of hand-picked editorial content.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters

video:more-like-this#

Model ensemble suitable for recommending similar assets on the asset detail page.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters
assetType#
enum
Default: movies+episodes

Asset type to be recommended.


excludeAlreadyWatched#
boolean
Default: false

Exclude already watched assets.

video:watch-next#

Model ensemble suitable for end-of-playback recommendations embedded in a video player.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters
assetType#
enum
Default: movies+episodes

Asset type to be recommended.


excludeAlreadyWatched#
boolean
Default: false

Exclude already watched assets.

video:episodes-list#

Model ensemble suitable for reverse-chronological listing episodes of a particular series.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters
excludeAlreadyWatched#
boolean
Default: false

Exclude already watched assets.

Smart personalized full-text search suitable for searching movies, series, and episodes.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters
assetType#
enum
Default: movies+episodes

Asset type to be recommended.


personalizationImpact#
enum
Default: medium

Determines whether personalization or full-text should have a higher priority in the ranking of the items.

video:search-segments#

Smart personalized full-text search suitable for searching segments (genres, actors, ...). A particular segmentation must exist beforehand.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

video:emailing#

Model ensemble suitable for sending personalized emails with personalized content for the user. It contains fine-tuned rotation settings so that the set of recommended assets for a particular user changes with each e-mail sent.

This Logic is not available in new databases by default. Please contact support@recombee.com if you want to use it.


Applicable to endpoints

Parameters
assetType#
enum
Default: movies+episodes

Asset type to be recommended.


rotationPolicy#
enum
Default: smart

A policy determining how strictly should already recommended items be rotated. The default option "smart" contains fine-tuned rotation settings, which take into account the position of recommended items and enable some of the items to re-appear in further e-mails. Option "total" never recommends items that have already appeared in previous e-mails.

Classified Advertising

Models tailored for classified advertising (real estate, automotive, electronics, services, etc.).

classifieds:homepage#

Model ensemble suitable for recommending products on homepage/welcome page of a Classified advertising site.


Applicable to endpoints

classifieds:personal#

Model ensemble suitable for recommending picked ads right for the given user. Can be used on different places, such as in-category recommendation when augmented with appropriate ReQL filter.


Applicable to endpoints

classifieds:similar-ads#

Model ensemble suitable for recommending ads that are similar to the one which is currently viewed.


Applicable to endpoints

Setting logic using API parameter#

The logic can be set per individual recommendation request by passing its name, such as ecommerce:homepage, to the logic parameter.

client.send(new recombee.RecommendItemsToUser('user-x', 10,
			{
				logic: 'ecommerce:homepage'
			}
			), callback
		);
val request = RecommendItemsToUser(
    userId = "user-x",
    count = 10,
    logic = Logic(name = "ecommerce:homepage")
)

val result = client.sendAsync(request)

result.onSuccess { recommendationResponse ->
    // Handle the successful response
}
...
client.send(new rqs.RecommendItemsToUser('user-x', 10, {'logic': 'ecommerce:homepage'}), callback);
result = client.send(RecommendItemsToUser("user-x", 10, logic="ecommerce:homepage"))
result = client.send(RecommendItemsToUser.new('user-x', 10, {:logic => 'ecommerce:homepage'}))
import com.recombee.api_client.bindings.Logic;

RecommendationResponse result = client.send(new RecommendItemsToUser("user-x", 10)
	.setLogic(new Logic("ecommerce:homepage"))
);
<?php
  $result = $client->send(new RecommendItemsToUser('user-x', 10, ['logic' => 'ecommerce:homepage']));
?>
using Recombee.ApiClient.Bindings;

RecommendationResponse result = client.Send(RecommendItemsToUser("user-x", 10,
		logic: new Logic("ecommerce:homepage")
	)
);
GET /my-db-id/recomms/users/user-x/items/?count=10&logic=ecommerce:homepage

Some logic can have parameters - if you specify just name, you will get some reasonable default, but you can tweak the parameters according to your particular needs.

client.send(new recombee.RecommendItemsToUser('user-x', 10,
    {
        logic: 
        {
        name: 'recombee:recently-viewed',
        settings: {
            maxAge: 3*24*60*60
        }
        }
    }), callback
);
val request = RecommendItemsToUser(
    userId = "user-x",
    count = 10,
    logic = Logic(name = "recombee:recently-viewed", settings = mapOf("maxAge" to 3 * 24 * 60 * 60))
)

val result = client.sendAsync(request)

result.onSuccess { recommendationResponse ->
    // Handle the successful response
}
...
client.send(new rqs.RecommendItemsToUser('user-x', 10,
                                        {'logic':
                                        {
                                            'name': 'recombee:recently-viewed',
                                            'settings': {
                                            'maxAge': 3*24*60*60
                                            }
                                        }
                                        }))
.then((recomms) => {
    console.log(recomms);
});
result = client.send(RecommendItemsToUser("user-x", 10,
    logic={"name": "recombee:recently-viewed",
        "settings": {
            "maxAge": 3*24*60*60
        }
    })
    )
result = client.send(RecommendItemsToUser.new('user-x', 10,
                        {'logic' => {
                            'name' => 'recombee:recently-viewed',
                            'settings' => {
                            'maxAge' => 3*24*60*60
                            }
                        }
                        }))
import com.recombee.api_client.bindings.Logic;

Logic l = new Logic("recombee:recently-viewed", new HashMap<String, Object>() {
    { put("maxAge", 3*24*60*60); }
});

RecommendationResponse result = client.send(new RecommendItemsToUser("user-x", 10)
.setLogic(l));
<?php
$result = $client->send(new RecommendItemsToUser('user-x', 10,
                ['logic' => [
                    'name' => 'recombee:recently-viewed',
                    'settings' => [
                    'maxAge' => 3*24*60*60
                    ]
                ]
                ]));
?>
using Recombee.ApiClient.Bindings;

Logic l = new Logic("recombee:recently-viewed",
                    new Dictionary<string, object>() {
                        {"maxAge", 3*24*60*60}
                    }
);
var recommendationResponse = client.Send(new RecommendItemsToUser("user-x", 5, logic: l));
POST /my-db-id/recomms/users/user-x/items/

Body:
{
"count": 10,
"logic": {
    "name": "recombee:recently-viewed",
        "settings":{
        "maxAge": 259200
        }
}
}
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