Glossary

Recommendation Engine

In one line: A recommendation engine suggests products to a customer based on what they (and similar customers) have looked at, bought, or shown interest in.

How it works

Most recommendation engines fall into three families. Collaborative filtering recommends what similar customers bought (“people who bought X also bought Y”). Content-based recommends items similar to ones the customer already engaged with (“you liked this jacket, here are others with similar attributes”). Hybrid systems blend both, plus business rules like inventory level, margin, or compatibility.

Why generic engines underperform in specialist retail

Off-the-shelf widgets trained on general e-commerce data often miss what makes your category unique. In electronics, that’s compatibility. In clothing, that’s sizing and style. In education, that’s curriculum fit. The lift you get from a properly tuned engine is dramatically larger than from a generic one.

What you need to use it

• A clean product catalog with structured attributes (specs, tags, categories)
• Customer behavior data — views, cart adds, purchases, and ideally returns
• A way to display recommendations on product pages, cart, and email

Related terms

Personalization, Cold-Start Problem, Customer Lifetime Value