landing_page/qdrant-landing/content/solutions/recommendation-engine.md

title, icon, image, source_link, demo_link, tutorial_link, custom_link, custom_link_name, weight

title	icon	image	source_link	demo_link	tutorial_link	custom_link	custom_link_name	weight
Recommendation engines	advertising							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.