Recommendation Engines: Beyond "Customers Also Bought"
Sarah Rodriguez
Feb 18, 2026
8 min read
E-commerce
Most e-commerce sites have basic recommendations: "customers also bought" or "similar items." These are fine but leave a lot of value on the table. Better recommendations can meaningfully impact revenue.
The simplest approach is collaborative filtering—people who liked X also liked Y. It works but has limitations. New items have no interaction history (the cold start problem). Popular items get over-recommended. It can't explain why something is recommended.
Content-based filtering uses item attributes instead of user behavior. If you liked sci-fi movies, here are more sci-fi movies. This helps with cold start but tends to over-recommend similar items and miss unexpected connections.
Modern systems combine both approaches in hybrid models. They also incorporate context (time of day, device, current session behavior), business rules (promote high-margin items, don't recommend out-of-stock), and diversity (don't show ten versions of the same thing).
Real-time recommendations are increasingly important. What someone looked at two months ago is less relevant than what they looked at two minutes ago. Session-based models that update in real-time outperform batch-processed recommendations.
Measure the right things. Click-through rate is easy to track but can be gamed. Better metrics: did they buy? did they return? did they come back? Short-term engagement doesn't always correlate with long-term value.
Finally, recommendations should feel helpful, not pushy. There's a fine line between "you might also like" and "we're tracking everything you do." Transparency and user control matter.
The simplest approach is collaborative filtering—people who liked X also liked Y. It works but has limitations. New items have no interaction history (the cold start problem). Popular items get over-recommended. It can't explain why something is recommended.
Content-based filtering uses item attributes instead of user behavior. If you liked sci-fi movies, here are more sci-fi movies. This helps with cold start but tends to over-recommend similar items and miss unexpected connections.
Modern systems combine both approaches in hybrid models. They also incorporate context (time of day, device, current session behavior), business rules (promote high-margin items, don't recommend out-of-stock), and diversity (don't show ten versions of the same thing).
Real-time recommendations are increasingly important. What someone looked at two months ago is less relevant than what they looked at two minutes ago. Session-based models that update in real-time outperform batch-processed recommendations.
Measure the right things. Click-through rate is easy to track but can be gamed. Better metrics: did they buy? did they return? did they come back? Short-term engagement doesn't always correlate with long-term value.
Finally, recommendations should feel helpful, not pushy. There's a fine line between "you might also like" and "we're tracking everything you do." Transparency and user control matter.
Written by
Sarah Rodriguez
AI Engineer at APPTAILOR