Recommendation Strategies
In this topic, you'll learn about:
Strategies
Recommendation strategy comprises:
- An algorithm that produces recommendations.
- Filters to curate the item/content recommendations based on the item/content attributes.
- Fallback items that are always recommended in case there are no recommendations that can be presented.
Recommendation models/algorithms
This section explains how each algorithm produces recommendations.
Popularity-based models
1. Most Viewed
| Purpose of the Model |
This model returns as recommendations items that were most frequently viewed. |
| Model Logic |
Every item's score is calculated as below:
|
| Model Output | Items are then ordered in descending order of their score, and the top 12 items that are eligible for recommendations and that satisfy filtering conditions are returned in the content generator response. The model may produce less than 12 recommendations if there are less than 12 items in the inventory file that are eligible for recommendations and that satisfy the filtering conditions. In this case, all such items are returned in the content generator response. |
2. Best Sellers
| Purpose of the Model | This model returns, as recommendations items that were most frequently bought. |
| Model Logic |
Every item's score is calculated as below:
|
| Model Output | Items are then ordered in descending order of their score, and the top 12 items that are eligible for recommendations and that satisfy filtering conditions are returned in the content generator response. The model may produce less than 12 recommendations if there are less than 12 items in the inventory file that are eligible for recommendations and that satisfy filtering conditions. In this case, all such items are returned in the content generator response. |
3. Trending - views
| Purpose of the Model | This model returns as recommendations items that were most frequently viewed in the last 7 days. |
| Model Logic |
Every item's score is calculated as below:
|
| Model Output | Items are then ordered in descending order of their score, and the top 12 items that are eligible for recommendations and that satisfy filtering conditions are returned in the content generator response. The model may produce less than 12 recommendations if there are less than 12 items in the inventory file that are eligible for recommendations and that satisfy the filtering conditions. In this case, all such items are returned in the content generator response. |
4. Trending - purchases
| Purposes of the Model | This model returns as recommendations items that were most frequently bought in the last 7 days. |
| Model Logic |
Every item's score is calculated as below:
|
| Model Output | Items are then ordered in descending order of their score, and the top 12 items that are eligible for recommendations and that satisfy filtering conditions are returned in the content generator response. The model may produce less than 12 recommendations if there are less than 12 items in the inventory file that are eligible for recommendations and that satisfy filtering conditions. In this case, all such items are returned in the content generator response. |
Co-occurrence based models
1. Viewed this, Viewed that
| Purpose of the Model | This model returns as recommendations items that were most frequently viewed together with the item being currently viewed (the ID of this item should be passed in the recommendations request to the content generator). |
| Model Logic | For every item, its score is calculated depending on the number of visitors who viewed both this item and the currently viewed item. |
| Model Output | Items are then ordered in descending order of their score, and the top 23 items are selected. 12 recommendations in the content generator response are first filled in with items from those 23 items that satisfy filtering conditions. The remaining empty slots are then filled with recommendations built by the Most Viewed model that satisfy filtering conditions and do not duplicate already selected items. The model may produce less than 12 recommendations if there are less than 12 items in the inventory file that are eligible for recommendations and that satisfy filtering conditions. In this case, all such items are returned in the content generator response. The Viewed this, Viewed that model is designed to work on Product Detail Pages (PDPs) where the item being currently viewed is present and its ID can be passed to the model as a parameter. Note that if the model receives an ID that is not present in the inventory file or does not receive the ID of the currently viewed item, then it returns recommendations built with the help of the Most Viewed model. |
2. Viewed this, Bought that
| Purpose of the Model | This model returns as recommendations items that were most frequently bought by visitors who also viewed the currently viewed item (the ID of this item should be passed in the recommendations request to the content generator). |
| Model Logic | For every item, its score is calculated depending on the number of visitors who both bought this item and viewed the currently viewed item. |
| Model Output | Items are then ordered in descending order of their score, and the top 23 items are selected. 12 recommendations in the content generator response are first filled with items from those 23 items that satisfy filtering conditions. The remaining empty slots are then filled in with recommendations built by the Most Viewed model that satisfy filtering conditions and do not duplicate already selected items. The model may produce less than 12 recommendations if there are less than 12 items in the inventory file that are eligible for recommendations and that satisfy filtering conditions. In this case, all such items are returned in the content generator response. |
The Viewed this, Bought that model is designed to work on PDPs where the item being currently viewed is present and its ID can be passed to the model as a parameter. Note that if the model receives an ID that is not present in the inventory file or does not receive the ID of the currently viewed item, then it returns recommendations built with the help of the Most Viewed model.
3. Bought this, Bought that
| Purpose of the Model | This model returns as recommendations items that were most frequently bought together with the currently viewed item (the ID of this item should be passed in the recommendations request to the content generator). |
| Model Logic | For every item, its score is calculated depending on the number of visitors who bought both this item and the currently viewed item. |
| Model Output |
Items are then ordered in descending order of their score, and the top 23 items are selected. 12 recommendations in the content generator response are first filled with those 23 items that satisfy filtering conditions. The remaining empty slots are then filled with recommendations built by the Best Sellers model that satisfy filtering conditions and do not duplicate already selected items. The model may produce less than 12 recommendations if there are less than 12 items in the inventory file that are eligible for recommendations and that satisfy filtering conditions. In this case, all such items are returned in the content generator response. |
The Bought this, Bought that model is designed to work on PDPs where the currently viewed item is present and its ID can be passed to the model as a parameter. Note that if the model receives the ID that is not present in the inventory file or does not receive the ID of the currently viewed item, then it returns recommendations built with the help of the Best Sellers model.
User-history based models
1. Last Viewed
Note: This model ignores any configured filtering conditions.
| Purpose of the Model | This model returns as recommendations 9 items that were most recently interacted with, by the visitor. |
| Model Logic | Interaction is classified as a view or purchase of an item. Items in recommendations are ordered by their interaction recency, with the most recently interacted item holding the first position. |
| Model Output | The model can return up to 9 recommendations in the content generator response, but no recommendations are returned for a visitor who has not previously interacted with any item. |
2. 1-1 Recommendations
| Purpose of the Model |
For a visitor with history of interactions with items on the client website, the algorithm produces recommendations based on visitor history and some similarity between items that is calculated based on past user-item interactions on the website. More recent items in visitor history have higher importance when generating recommendations. |
| Model Logic |
This algorithm belongs to the family of collaborative filtering approaches. Its hyper-parameters are selected via regularly performed offline validation procedure with the goal to maximize the chances of predicting the next item the visitor will interact with. This ensures the best algorithm is selected for the client. |
To configure 1-1 Recommendations for email for re-targeting use cases, you can do either of the following:
Fallback items
Fallback items are standard or commonly recommended items pre-selected by the marketers. These items are displayed when recommendation filter criteria does not yield any results. This option is very useful and kicks in when customers don’t yet have any interaction (views or purchase) history, or when there are no items that match the marketer’s campaign recommendation filter criteria.
This option will ensure some items/content are always displayed in the recommendations widget even when the algorithms along with the filters applied do not yield any results. These can be entered as individual items in the strategy.
For example, let us say we are using a most purchased model, which uses the user’s purchase history to make recommendations of similar items that they may purchase.
However, what happens in the scenario when there is no data available to build the recommendations? One possibility is that it is the user’s first visit on the site, and has not purchased any items yet.
In such a scenario, it is important that there is a fallback mechanism in place, and we can show the user some Item recommendations. The marketer could set these or maybe they could use a most popular model for the fallback items, so that the space occupied by the recommendations could result in a customer interaction and not go to waste.
