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

title, icon, tabid, landing_image, image, image_caption, source_link, demo_link, tutorial_link, custom_link, custom_link_name, weight, short_description

title	icon	tabid	landing_image	image	image_caption	source_link	demo_link	tutorial_link	custom_link	custom_link_name	weight	short_description
Recommendations	advertising	recommendations	/content/images/abstract_cubes_3.svg	/content/images/recommendations.png	History-based Recommendations						30	User behavior can be represented as a semantic vector is similar way as text or images. Qdrant allows you to create a recommendation engine with custom filters and real-time updates. No need to deploy a MapReduce cluster.

User behavior can be represented as a semantic vector is similar way as text or images. This vector can represent user 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. Understand user behavior in real time.