Universal
recommendation system to help businesses improve key metrics
Works with different types of content in different areas of business
How does the recommendation system affect business?
Loyalty growth
Increase LTV
Reduced outflow
Average check growth
Builds a multilayer model of a digital twin
Consumer profile
HRNN
Baseline
statistical
ranking
ranking
Context
Collaborative
filtering
filtering
Baseline
popularity
Builds trust
Forms a model of a digital
twin, understands what
what kind of person he is and what
he likes
It becomes easier to recommend
new content or services,
and existing services are more
likely to fall within the user's
expectations. These builds trust in
the recommendation system and
increases the pleasure of using
your service, increasing the user's
immersion
twin, understands what
what kind of person he is and what
he likes
It becomes easier to recommend
new content or services,
and existing services are more
likely to fall within the user's
expectations. These builds trust in
the recommendation system and
increases the pleasure of using
your service, increasing the user's
immersion
Data Model Recommendations
Our system passes the user’s trust threshold, increases the content discovery success rate, and is more likely to hit user needs
Five recommendation modules
1
Relevant recommendations based on recent interactions
2
Statistics based recommendations
3
Recommendations based on recent product or content views
4
Recommendations that show your favorite products, services, or content
5
Overcoming Cold Start in Recommendations
Integration
Integration with API
You send us the data, and in return we send you recommendations
In-house
They write about us
Write to us
We will contact you