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

title, icon, tabid, landing_image, image, image_caption, default_link, default_link_name, weight, short_description, sitemapExclude

title	icon	tabid	landing_image	image	image_caption	default_link	default_link_name	weight	short_description	sitemapExclude
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.	True

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.