Some recommendation types use behavioral data from your shoppers to train machine learning models to build personalized recommendations. Other recommendation types use catalog data only and do not use any behavioral data. If you want to start quickly, you can use the following, catalog-only recommendation types:
More like this
So when can you start using recommendation types that use behavioral data? It depends. This is referred to as the Cold Start problem.
The Cold Start problem is a measure of how much time that a model needs to train before it can be considered high quality. In product recommendations, it translates to waiting for Adobe Sensei to train its machine learning models before deploying recommendation units on your site. The more data that these models have, the more accurate and useful the recommendations are. Collecting this data takes time and varies based on traffic volume. Because this data can be collected only on a production site, it is in your best interest to deploy data collection there as early as possible. You can do this by installing and configuring the
The following table provides some general guidance for the amount of time that it takes to collect enough data for each recommendation type:
|Recommendation type||Training Time||Notes|
||Varies||Depends on volume of events - views are most common, and therefore learns faster; then adds to cart, then purchases|
||Requires more training||Product views are decently high in volume|
||Requires the most training||Purchase events are the most rare events on commerce site, especially compared to product views|
||Requires three days of data to establish a popularity baseline||Trending is a measure of recent momentum in a product’s popularity compared with its own popularity baseline. A product’s trending score is computed using a foreground set (recent popularity over 24 hours) and a background set (popularity baseline over 72 hours). If an item has become much more popular within the last 24 hours as compared with its baseline popularity, then it receives a high trending score. Every product has this score, and the highest ones at any time comprise the set of top trending products.|
Other variables that can impact the time needed to train:
To help you visualize the training progress of each recommendation type, the create recommendation page displays readiness indicators.
While data is collected on production and machine learning models are trained, you can implement the remaining tasks necessary to deploy recommendations to your storefront. By the time you have finished testing and configuring recommendations, the machine learning models have collected and computed enough data to build relevant recommendations thus allowing you to deploy the recommendations to your storefront.
If there is not sufficient input data to provide all requested recommendation items in a unit, Adobe Commerce provides backup recommendations to populate recommendation units. For example, if you deploy the
Recommended for you recommendation type to your homepage, a first-time shopper on your site has not generated enough behavioral data to accurately recommended personalized products. In this case, Adobe Commerce surfaces items based on the
Most viewed recommendation type to this shopper.
The following recommendation types fallback to
Most viewed recommendation type if there is not sufficient input data collected:
Recommended for you
Viewed this, viewed that
Viewed this, bought that
Bought this, bought that
Conversion (view to purchase)
Conversion (view to cart)