Reference models

Luigi's Box offers wide scale of standard recommedender models. Each model needs to be trained first. Once everything is set up, you will be given the recommender_type to use in recommendation requests described in API section. Please contact our support to discuss the options in detail. If needed, these models can be customized to your specific use case, or, we can prepare fully customized models. The table below lists the standard models.

Each recommender is personalized for users who granted cookie consent. The models consider user preferences and boost items with characteristics similar to user's past interactions.

Recommender type	Short description	Location	Input
`test_reco`	Simple, most popular recommender to test client requests.	Arbitrary	None
`item_detail_alternatives`	Similar products / alternatives recommender preferring slightly more expensive products compared to input product (upsell).	Product detail page (PDP)	product resource identifier, user identifier (optional)
`item_detail_complements`	Related products usually bought together (cross-sell) with input product. By default, recommender prefers cheaper products (complements). Products are diversified by category to offer variety of different products if possible. General item detail complements model fits to the most industry segments. Some specific segments have their own model (described below).	Product detail page (PDP)	product resource identifier, user identifier (optional)
`basket`	Related products usually bought together (cross-sell) with all products already in the basket. By default, recommender prefers cheaper products (complements). Products are diversified by category to offer variety of different products if possible. This general basket detail model fits to the most industry segments. Some specific segments have their own model (described below).	Basket detail	product resource identifiers, user identifier (optional)
`basket_popup`	Related products usually bought together (cross-sell) with a product most recently added into the basket. By default, recommender prefers cheaper products (complements). Products are diversified by category to offer variety of different products if possible. General basket popup model fits to the most industry segments. Some specific segments have their own model (described below).	Basket pop up window	product resource identifier, user identifier (optional)
`user_conversion_based`	Recommender offers products related (cross-sell) to previously purchased or trends (if user has no past interactions). The idea is to help user find more interesting products to buy.	Arbitrary, usually homepage, 404	user identifier
`user_click_based`	Recommender offers products similar to those clicked but not bought recently by the user / or trends (if user has no past interactions). The idea is to help user finish the purchase.	Arbitrary, usually homepage, 404	user identifier
`category`	Most relevant trending products, from input category for the user. Results are diversified to as much next level subcategories as possible.	Product listing page (PLP)	category resource identifier, user identifier (optional)
`brand`	Most relevant trendy products from input brand for the user. Results are diversified to as much categories as possible.	Product listing page (PLP)	brand resource identifier, user identifier (optional)
`top_categories`	Most relevant categories for the user. Used as a shortcut to user's preferred PLPs.	Arbitrary, usually homepage, 404	user identifier
`trends`	Most popular products. Could be used on homepage, in PLP to offer products from this category/brand only, in PDP (in combination with some filter) to offer e.g. popular products from the same collection. Could be ordered by different attribute than popularity (e.g., price_amount).	Arbitrary, usually homepage, 404	user identifier (optional), category / brand / product resource identifier (optional)
`news`	Novel products based on date of introduction. Could be used on homepage, in PLP to offer products from this category/brand only, in PDP (in combination with some filter) to offer e.g. novel products from the same collection.	Arbitrary, usually homepage, PLP	user identifier (optional), category / brand / product resource identifier (optional)
`news_by_label`	Popular products with defined label indicating novel products. Could be used on homepage, in PLP to offer products from this category/brand only, in PDP (in combination with some filter) to offer e.g. novel products from the same collection.	Arbitrary, usually homepage, PLP	user identifier (optional), category / brand / product resource identifier (optional)
`discount`	Discounted products based on discount amount. Could be used on homepage, in PLP to offer products from this category/brand only, in PDP (in combination with some filter) to offer e.g. discounted products from the same collection.	Arbitrary, usually homepage, PLP	user identifier (optional), category / brand / product resource identifier (optional)
`discount_by_label`	Popular products with defined label indicating discounted products. Could be used on homepage, in PLP to offer products from this category/brand only, in PDP (in combination with some filter) to offer e.g. discounted products from the same collection.	Arbitrary, usually homepage, PLP	user identifier (optional), category / brand / product resource identifier (optional)
`last_seen`	Products most recently visited by a user.	Arbitrary	user identifier
`article_items`	Most relevant products based on the input article (blog post).	Article detail page	article resource identifier, user identifier (optional)
`article_articles`	Most relevant articles based on the input article (blog post).	Article detail page	article resource identifier, user identifier (optional)
`random`	Random products. It gets more useful when combined with some characteristic, e.g., new products added within last month, discounted products, products from same brand as input product.	Arbitrary	user identifier (optional), category / brand / product resource identifier (optional)

As some industry segments have their specifics, we identified several segment specific models adjusting abovementioned models.

Recommender type	Industry segment	Short description	Location	Input
`item_detail_complements_books_games`	Books & Games	Related products usually bought together (cross-sell) with input product. Products are diversified by category to offer variety of different products if possible. In comparison to general complements model, it doesn't prefer cheaper products, also it allows recommendation from exactly same category.	Product detail page (PDP)	product resource identifier, user identifier (optional)
`basket_books_games`	Books & Games	Basket detail recommender modified for books & games industry segment. Related products usually bought together (cross-sell) with all products already in the basket. Products are diversified by category to offer variety of different products if possible. In comparison to general basket model, it doesn't prefer cheaper products, also it allows recommendation from exactly same category.	Basket detail	product resource identifiers, user identifier (optional)
`basket_popup_books_games`	Books & Games	Basket popup recommender modified for books & games industry segment. Related products usually bought together (cross-sell) with a product most recently added into the basket. Products are diversified by category to offer variety of different products if possible. In comparison to general basket popup model, it doesn't prefer cheaper products, also it allows recommendation from exactly same category.	Basket pop up window	product resource identifier, user identifier (optional)
`item_detail_complements_food_beverages`	Food & Beverages	Related products usually bought together (cross-sell) with input product. Products are diversified by category to offer variety of different products if possible. In comparison to general complements model, it prefers previously bought products and don't filter out expensive products.	Product detail page (PDP)	product resource identifier, user identifier (optional)
`basket_food_beverages`	Food & Beverages	Basket detail recommender modified for fast-moving consumer goods, typically from food and beverages industry segment. Related products usually bought together (cross-sell) with all products already in the basket. Products are diversified by category to offer variety of different products if possible. In comparison to general basket model, it prefers previously bought products and don't filter out expensive products.	Basket detail	product resource identifiers, user identifier (optional)
`basket_popup_food_beverages`	Food & Beverages	Basket popup recommender modified for fast-moving consumer goods, typically food and beverages industry segment. Related products usually bought together (cross-sell) with a product most recently added into the basket. Products are diversified by category to offer variety of different products if possible. In comparison to general basket popup model, it prefers previously bought products and don't filter out expensive products.	Basket pop up window	product resource identifier, user identifier (optional)
`item_detail_complements_luxury_goods_jewelry`	Luxury goods & Jewelry	Related products usually bought together (cross-sell) with input product. Products are diversified by category to offer variety of different products if possible. In comparison to general complements model, it prefers products of similar price.	Product detail page (PDP)	product resource identifier, user identifier (optional)
`basket_luxury_goods_jewelry`	Luxury goods & Jewelry	Basket detail recommender modified for luxury goods & jewelry industry segment. Related products usually bought together (cross-sell) with all products already in the basket. Products are diversified by category to offer variety of different products if possible. In comparison to general basket model, it prefers products of similar price.	Basket detail	product resource identifiers, user identifier (optional)
`basket_popup_luxury_goods_jewelry`	Luxury goods & Jewelry	Basket popup recommender modified for luxury goods & jewelry industry segment. Related products usually bought together (cross-sell) with a product most recently added into the basket. Products are diversified by category to offer variety of different products if possible. In comparison to general basket popup model, it prefers products of similar price.	Basket pop up window	product resource identifier, user identifier (optional)
`item_detail_complements_medical_cosmetics`	Pharma / Medical, Cosmetics & Body care	Related products usually bought together (cross-sell) with input product. By default, recommender prefers cheaper products (complements). Products are diversified by category to offer variety of different products if possible. In comparison to general complements model, it prefers products from more different categories.	Product detail page (PDP)	product resource identifier, user identifier (optional)
`basket_medical_cosmetics`	Pharma / Medical, Cosmetics & Body care	Basket detail recommender modified for medical, cosmetics & body care industry segments. Related products usually bought together (cross-sell) with all products already in the basket. Products are diversified by category to offer variety of different products if possible. In comparison to general basket model, it prefers products from more different categories.	Basket detail	product resource identifiers, user identifier (optional)
`basket_popup_medical_cosmetics`	Pharma / Medical, Cosmetics & Body care	Basket popup recommender modified for medical, cosmetics & body care industry segments. Related products usually bought together (cross-sell) with a product most recently added into the basket. Recommender prefers cheaper products (complements). Products are diversified by category to offer variety of different products if possible. In comparison to general basket popup model, it prefers products from more different categories.	Basket pop up window	product resource identifier, user identifier (optional)