What Is a Recommendation Engine?
A recommendation engine uses algorithms to suggest products, content, or actions based on user behaviour and preferences. Learn how it drives engagement and revenue.
Key Takeaways
- Recommendation engines analyse user behaviour and item characteristics to predict which products or content a user will find most relevant.
- The two primary approaches are collaborative filtering (based on similar users) and content-based filtering (based on item attributes).
- Recommendation engines drive 35% of Amazon's revenue and 75% of Netflix viewing, demonstrating their commercial impact.
How recommendation engines work
A recommendation engine collects data on user behaviour — views, purchases, ratings, clicks — and uses algorithms to predict what each user will want next. Collaborative filtering finds users with similar behaviour patterns and recommends items that similar users liked. Content-based filtering analyses item attributes and recommends items similar to what the user has already engaged with. Hybrid systems combine both approaches for greater accuracy.
Collaborative vs content-based filtering
Collaborative filtering works by identifying patterns across users. If User A and User B both bought products X and Y, and User B also bought product Z, the system recommends Z to User A. It does not need to understand what the products are — only that similar users liked them. Content-based filtering analyses product attributes: a customer who bought red running shoes might see recommendations for other red athletic footwear. Each method has strengths depending on data availability.
Business impact
Recommendation engines directly increase revenue, average order value, and customer retention. They reduce the effort customers spend searching for relevant products, improving satisfaction and engagement. For African ecommerce platforms like Jumia and Takealot, recommendations help customers navigate large catalogues efficiently. Even simple recommendation implementations — showing related products or frequently bought-together items — measurably increase sales per session.
Building vs buying
Small and mid-sized businesses should use pre-built recommendation solutions rather than building custom engines. Shopify, WooCommerce, and major marketplace platforms include recommendation features. Dedicated tools like Algolia Recommend and Recombee offer plug-and-play integration. Custom builds are justified only for businesses with unique data structures or recommendation logic that off-the-shelf tools cannot handle. Start simple and iterate based on measured performance improvement.