What is Recommendation Engine?
Turkish: Öneri Motoru (Recommendation Engine)
A recommendation engine ranks products, content, or actions for each user based on behavior, item features, and context.
What is Recommendation Engine?
A recommendation engine predicts what a user is most likely to find relevant. It can power product suggestions in an e-commerce site, content choices in a media app, or next-best actions in a B2B portal.
The system does not have to show only “best sellers.” It can combine the user’s past behavior, preferences of similar users, item attributes, stock availability, price range, and campaign context.
Types of Recommendations
- Content-based: Uses product or content attributes
- Collaborative filtering: Learns from behavior of similar users
- Rule-based: Applies business rules such as stock, margin, region, or campaign
- Hybrid models: Combine multiple approaches
Machine learning and embedding methods can capture similarity between products or users more flexibly.
Business Use
Recommendation engines are used in e-commerce, subscription products, content platforms, CRM, and customer support screens. The goal is not always more sales; it can also be helping users find the right product, showing agents a relevant help article, or reducing unnecessary choices.
Strong recommendation systems monitor not only click-through rate, but also satisfaction, returns, stock impact, and fair exposure.
Related Terms
An embedding represents text, images, products, or other data as numeric vectors that can be compared for semantic similarity.
E-CommerceE-commerce is the digital sale and management of products or services through websites, marketplaces, mobile apps, and connected systems.
Machine LearningMachine learning trains models on data patterns so software can make predictions, classifications, or decisions on new examples.