Recommendation Templates

In this article, we provide descriptions of all existing recommendations models in Exponea app under Campaigns > Recommendations > Create new. Some of them have are labelled as [BETA] since we released them only recently and are still under usage testing.

Under each model, we provide a list of following properties:
Type: Whether the model is “Rule-based” or “AI model” and whether it is "personalised" or "non-personalised".

  • Rule-based: usually a simple statistical technique. These models are ready to serve immediately after saving the template.
  • AI model: driven by a machine learning algorithm. We run the training of algorithms over the night, so you have to wait for the trained model until the morning.
  • Non-personalised: model is searching for the best items that are shown to all customers. Such a model can be driven by events (like "top-selling products") or by catalog metadata (gender, categories, colour, etc.)
  • Personalised: model is searching for hidden behavioural similarities between customers and offers items tailored to each individual customer. These models are driven by bigger load of events (at least a few months timeframe) but can also take advantage of catalog metadata.

Usage: For which e-commerce problem is the model useful for
Requirements: set of requirements needed for model working
Requesting: how to request your recommendations from the saved model. The standards are described in more detail here: Integration chapter. On the top of the standards, we support including the currently viewed item ID and category names in the body of the request.
Functionality: description of the inner logic

Simple non-personalised templates

Popular right now

Type: Rule-based, non-personalised
Usage: Top selling or viewing products
Requirements: events tracking, imported catalog
Requesting: standard
Functionality: Shows products sorted by the count of one selected popularity event (typically purchase_item or view_item).

New items

Type: Rule-based, non-personalised
Usage: Promote newest products
Requirements: imported catalog containing datetime (or any numerical column describing date of when the product was added).
Requesting: standard
Functionality: Shows sorted products in descending order that were recently added to the selected product catalog.

Similar items

Type: Rule-based, non-personalised
Usage: Present products from the same brand, category or style (depending on selected columns from the product catalog) on product detail page.
Requirements: imported catalog, ideally rich in attributes
Requesting: The currently viewed item must be included in the request.
Functionality: Shows similar items to a given product based on catalog attributes you pick, such as brand or category. We allow you to set “required” and “optional” attributes that can be taken into consideration while computing the similarity. Additionally, you can also specify the “overlap size” determining the minimum overlap of the optional attributes.

Advanced non-personalised templates

Customers who bought this item also bought

Type: AI model, non-personalised
Usage: Up-selling, cross-selling complementary products on product detail page or basket page. Also useful in reactivation emails.
Requirements: events tracking, imported catalog, at least 6 months of purchase_item history
Requesting: The currently viewed item can be included in the request.
Functionality: Shows items that are the most frequently bought together with the currently viewed item by the customer. Typically, the recommended items are complementary, such as accessories to a smartphone.
For the use-case of showing complementary products in the shopping cart, it is suggested to include the product ID which was added to the cart.

Metric based category [BETA]

Type: Rule-based, non-personalised
Usage: Top selling products within certain categories on the category page or product detail page
Requirements: event tracking, imported catalog
Requesting: add a list of desired category names to the request (see Integration chapter & Product discovery use-case)
Functionality: Shows products only from the given category (or categories) that are sorted according to the chosen metric - e.g. a number of purchases or views. Naturally, we sort items from the highest metric value to the lowest one. After saving the model, all calculations last a few seconds, then the results are stored and re-calculated periodically.

  • If item ID is included in the request, the AI model finds the top 50 most co-purchased products for a given item, sort them according to the co-purchase count and return. If there are no historical co-purchases, model fallbacks to the “Popular right now” technique with a purchase count metric.
  • If not:, the last purchased items by the customer are retrieved and run the “If included” steps for this item.

Notes: For minimization of recommendation latencies, we calculate your metric for every category (unique categories from category identifier defined in template settings) immediately after engine save.
In practice, if you have made a change to settings it can take some time to come into effect. If you are in need of immediate change, we suggest creating a new engine.

If there is no historical purchase at all, we use the same “Popular right now” fallback.

Text model

Type: AI model, non-personalised
Usage: Present products on the Product detail page that are consistent and similar to the item currently viewed.
Requirements: imported catalog containing textual information describing items. (e.g. attributes such as “description”, "name", "color", etc.)
Requesting: The currently viewed item must be included in the request.
Functionality: Shows products in order from the most similar one to the currently viewed by a customer. We are using the TF-IDF model to search for the similarities in terms of word frequencies. Additionally, you can choose attributes that need to exactly shared among recommended items and the requested item.

Simple personalized templates

Set of personalized recommendation models that are easy to setup.

Customer recent interactions

Type: Rule-based, personalised
Usage: Recommending products the customer has recently interacted with ("Continue where you left off" on popup banner, "Finish your order")
Requirements: events tracking, imported catalog
Requesting: standard
Functionality: Shows the most recent products the customer was interacting with based on the chosen interaction event (view_item, purchase_item, cart_update). Products are sorted from the most recent timestamps to the oldest ones.

Homepage

Type: AI model, personalised
Usage: Providing personalised recommendations on Homepage based on historical activity
Requirements: events tracking, imported catalog, at least 2 months of purchase_item, view_item and cart_update history
Requesting: standard
Functionality: This template reuses logic of Personalized recommendation for you template (see in Advanced personalised templates) and is optimized for Homepage in terms of automatically selected learning window and real-time support of recently tracked events.

Product detail

Type: AI model, personalised
Usage: Showing alternative products to consider that were visited together by all customers on Product detail page
Requirements: events tracking, imported catalog, at least 2 months of view_item history
Requesting: The currently viewed item can be included in the request
Functionality: This template reuses logic of Customers who viewed this item also viewed template (see in Advanced personalised templates) and is optimized for Product detail page in terms of automatically selected learning window.

Advanced personalised templates

Personalized recommendations for you

Type: AI model, personalised
Usage: Cross-sell, Personalized recommendations for you, we think you would like these, Based on your recently viewed products, Homepage
Requirements: events tracking, imported catalog, at least 2 months of purchase_item and view_item history
Requesting: The currently viewed item can be included in the request.
Functionality: Shows products matching customer preferences based on purchasing and the browsing history, including real-time events in the current session. These events define the user-item interaction matrix. The more users interact with the same items, the more we consider them similar.

  • If item ID is included in the request the AI model finds the top similar products to the requested product and rank them by their similarity score. The similarity score is calculated using their shared events (bought, add to cart and viewed together). If there are too few shared interactions, the model fallbacks to “Popular right now” technique where popularity is computed from interaction events defined in the template.
  • If not we take the user’s interaction history (purchases, views, cart updates) and the AI model finds top similar users. Within similar users, the model looks for items with the highest probability of interaction for a requested user and returns them as recommendations.
    If there are too few historical interactions, the model retrieves the last 3 viewed products from App and run the “If included” steps for these items. Recommendations are then sorted from newest to oldest according to the retrieved products.
    If there is no historical purchase at all, we use again the “Popular right now” fallback with the popularity given as the event with most interactions.

Customers who viewed this item also viewed

Type: AI model, personalised
Usage: Alternative products to consider, Similar products, You might also like these, Product detail page.
Requirements: events tracking, imported catalog, at least 2 months of view_item history
Requesting: The currently viewed item can be included in the request.
Functionality: Shows items that are chronologically visited together by all users based on the item currently viewed. Typically the AI model recommends alternative products while the customer is browsing through a specific category or set of products.

  • If item ID is included in the request the AI model finds the top co-occurred products based on the user’s session and sort them by relevance. If there are too few historical views, the model will recommend most viewed products.
  • If not, we take the user’s last 3 viewed products tracked by Exponea and run the “If included” steps for these items. Recommendations are then sorted chronologically according to the retrieved products.
    If there is no historical viewed item, we use again the “most viewed products” fallback.

Personalized category page [BETA]

Type: AI model, personalised
Usage: Cross-sell, Personalized recommendations for you, We think you would like these, Personalized category recommendation, Personalized ranking of your category page, Category page, Homepage
Requirements: events tracking, imported catalog, at least 2 months of purchase_item, view_item and cart_update history, catalog containing category and datetime
Requesting: customer and a list of desired categories
Functionality: Shows products from specified category/categories ranked by customer preferences based on purchasing and the browsing history. These events define the user-item interaction matrix. The more users interact with the same items, the more we consider them similar. Individual products in category are ranked based on user preferences. For example, if a given customer prefers dark chocolate, our engine finds other similar customers based on this preference and recommends items that they like. Furthermore, items are sorted based on the frequency of purchases of other users and only items that the customer has not purchased yet are recommended. As a fallback (for customers with only few purchases - customers for which we are not able to distinguish their style), we recommend attractive products. Attractive/trending products are currently most viewed & purchased products.

Fallback templates present in Advanced template

These are models intended as a fallback (support model in case the main one fails to personalise) while designing a combination of models in Advanced template.

Manual selection

Type: Rule-based, non-personalised
Usage: Promote a certain, manually-chosen set of products (sponsored, on-sale, etc.)
Requirements: just imported catalog
Requesting: standard
Functionality: Shows products picked manually by their IDs. All customers will see the same items.

Chosen by metric

Type: Rule-based, non-personalised
Usage: Top selling or viewing products
Requirements: event tracking, imported catalog
Requesting: standard
Functionality: Shows products sorted according to chosen metric - e.g. the number of purchases or number of views. Naturally, we sort items from the highest metric value to the lowest one. The whole calculation can take a few seconds, then the results are stored and re-calculated periodically. Note that the calculation is subject to a 5-minute cache (results are updated every 5 minutes).


What´s next?

Advanced engine

Recommendation Templates


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