Files
landing_page/qdrant-landing/content/solutions/recommendation-engine.md
T
2021-04-03 22:04:28 +02:00

20 lines
631 B
Markdown

---
title: Recommendation engines
icon: advertising
image:
source_link:
demo_link:
tutorial_link:
custom_link:
custom_link_name:
weight: 30
---
Users, like text and pictures, can be represented as a semantic vector.
This vector can represent the user's preferences, behavior patterns, or interest in the product.
With Qdrant, user vectors can be updated in real-time, no need to deploy a MapReduce cluster.
Besides, Qdrant will allow you to place arbitrary restrictions on recommendations.
What if the user signed up 3 days ago and premium offers are in effect for him?
Qdrant will be able to handle this and such conditions.