Skip to content
EKG Catalog
Recommendation Engine
EKGF/ekg-catalog

Recommendation Engine

Recommendation Engine Behavioral Patterns Content Relationships
Recommendation Engine Behavioral Patterns Content Relationships
New to this diagram? Learn about the method: Use Case Tree · Use Case · Outcome

Recommendation engines analyze user behavior, content relationships, and social signals to deliver personalized suggestions for products, services, or content. This use case enables organizations to increase engagement and conversion by understanding complex, multi-dimensional relationships between users, items, and behaviors that evolve over time.

The Challenge

Modern recommendation systems need to:

  • Understand user interests and preferences across multiple dimensions
  • Model relationships between content, products, and user behaviors
  • Adapt to changing user preferences and content relationships
  • Provide real-time recommendations based on current context
  • Handle cold-start problems for new users and content
  • Balance exploration and exploitation in recommendations

Traditional approaches struggle with the complexity of multi-dimensional relationships and real-time adaptation.

Why EKG is Required

Effective recommendations require understanding complex, multi-dimensional relationships that evolve over time. EKG enables:

  • Graph-based relationship modeling — Natural representation of user-content-product relationships
  • Semantic understanding — Ontologies enable understanding of content meaning and user intent
  • Real-time queries — Graph queries enable real-time recommendation generation
  • Multi-dimensional relationships — Graph structure supports complex relationship types (interests, behaviors, social, temporal)
  • Dynamic adaptation — Graph structure can evolve as relationships change
  • Cold-start handling — Semantic relationships enable recommendations even for new users/content

Business Value

  • Increased engagement — Better recommendations drive more interaction
  • Higher conversion rates — Relevant recommendations lead to more conversions
  • Improved user experience — Personalized experience increases satisfaction
  • Better content discovery — Users find relevant content more easily
  • Competitive advantage — Superior recommendations differentiate the platform

Components

Recommendation engines leverage multiple data sources and models:

  • Client 360 - Customer understanding and personalization
  • Social Media - Social graph and interest graph components