Recommendation types
Similar items
This type of recommendation suggests items similar to the one that is currently viewed. The purpose is to offer customers a wider selection of items with similar features (for example, the same category).
The recommendation model can be trained by using only the item feed, but once events (for example page visits) become available, the model starts using the all available data from the event history to find items that are similar based on visitor interactions.
The AI model for this type is trained weekly by default, this can be changed.
Cross-sell and cart recommendations
The AI model analyzes the transaction history of the workspace to find which items are frequently bought together. Using this information, you can:
- use cross-sell recommendations to suggest other items that are likely to be bought with the item that is currently being viewed by the customer.
- use cart recommendations to suggest items that are likely to be bought with the item or items that are in the customer’s cart.
If an item has not yet been purchased with other items, the model predicts what would be recommended with items similar to the item or items which are the context of the recommendation.
The AI model for these types is trained weekly.
Personalized recommendations
This type of recommendation lets you suggest the items on the basis of a visitor’s buying preferences and their behavioral profile. In other words, items are recommended on the basis of what that visitor browsed or bought. As a result, the customer doesn’t get lost in item overload. In order to prepare the recommendations, the system analyzes page visits, transactional data, and item feeds (for example, the system can propose a category of items, such as computers, TVs, and books, because the customer purchased or viewed similar items in the past).
If a customer’s history has no item page visit events or transactions, a recommendation is generated on the basis of the first items clicked by in the last 90 days by other first-time vistors.
The AI model for this type is trained weekly.
Visual similarity
The purpose of this recommendation type is to help customers make purchase decisions faster by showing visually similar items (shape, color, style, etc.). To prepare recommendations, the AI model analyzes the images added to items in the feed. On this basis, it prepares offers of similar items.
The AI model for this type is trained weekly.
Last seen
This type of recommendation is used to display the items which have been viewed recently by a particular user. To prepare recommendations, the system analyzes page.visit and product.view events and in response it displays the items which have been viewed by a particular customer.
Top items
The purpose of this type of recommendation is to display top items according to a metric you select, such as bestsellers of the last 30 days, items which have been viewed the most during the last 30 days, etc. The metrics are re-calculated daily.
Item comparison
This type of recommendation is an extension of the similar items recommendation - apart from displaying items similar to the context item (currently viewed item), it shows the attributes of similar and viewed items in a table which makes them easy to compare with the current item.
The AI model for this type is trained weekly by default, this can be changed.
Recent interactions
This type of recommendation uses data from an aggregate to promote items towards which the customers performed a specific action which you select. Apart from these most common, such as a visit to the item page, adding the item to the cart, marking the item as favorite, or purchase, you can select any measurable event related to the items you offer.
Section page
This type of recommendation allows you to display a personalized section with items that share attributes such as the category, brand, style, collection, and so on. The motif (item feature/attribute) of the section is personalized as well as the items selected for the section. This way you can personalize the whole page. The model uses the customer’s behavioral profile (page visits and transactions) to generate the recommendation.
This type of recommendation requires a meta-catalog which store data about the attributes.
The AI model for this type is trained weekly.
Attributes
This type of recommendation allows you to promote the features of the items, such as brand, styles, categories or any kind of attributes which are selected for each customer individually. Unlike the section recommendation, this type recommends only attributes, without suggesting any particular items. The model uses the customer’s behavioral profile (page visits and transactions) to generate the recommendation.
The AI model for this type is trained weekly.
Recommendation model summary
The table that explains how recommendations of each type are generated (the source and context needed):
| Scenario | AI engine | Metric-based | Customer context | Item context | Multiple item context | No context |
|---|---|---|---|---|---|---|
| Personalized |  |
|
||||
| Similar items | |
|
||||
| Visual similarity | |
|
||||
| Cross-sell | |
|
||||
| Cart recommendations | |
|
||||
| Last seen | |
|
||||
| Top items | |
|
||||
| Item comparison | |
|
||||
| Recent interactions | |
|
||||
| Section | |
|
||||
| Attributes | |
|
Application of recommendations
The table below presents the application of recommendation types in business scenarios:
| Scenario | Home page | Category/brand page | Item page | Add to cart | Checkout | Zero search results | Post-purchase |
|---|---|---|---|---|---|---|---|
| Personalized | |
|
|
|
|
|
|
| Similar items1 | |
||||||
| Visual similarity1 | |
||||||
| Cross-sell1 | |
|
|||||
| Cart recommendations1 | |
|
|||||
| Last seen | |
|
|
|
|
|
|
| Top items | |
|
|
|
|
|
|
| Item comparison1 | |
||||||
| Recent interactions | |
|
|
|
|
|
|
| Section | |
|
|
|
|
|
|
| Attributes | |
|
|
|
|
|
|
1Recommendations with an item context can be displayed in messages and pages other than the item page. To do so, you need to use an aggregate to retrieve the context (for example, the last bought item).
