Interest Graph
Interest graphs model relationships between customers and topics, brands, products, and content to understand what people care about. This use case addresses extracting implicit interests from behavior, tracking how interests evolve over time, integrating signals across multiple channels, handling cold-start problems for new customers, and enabling hyper-personalized experiences through semantic interest hierarchies and cross-domain inference.
The Challenge¶
Organizations struggle to understand and leverage customer interests:
- Implicit vs. explicit interests — Most interests are revealed through behavior, not stated preferences
- Interest evolution — Customer interests change over time, requiring continuous learning
- Context dependency — Same person has different interests in different contexts (work vs. personal)
- Cross-channel fragmentation — Interest signals scattered across web browsing, purchases, social media, and content consumption
- Cold start problem — Difficulty understanding interests for new customers with limited history
- Interest granularity — Balancing broad categories with specific niche interests
- Privacy and consent — Managing interest data while respecting privacy preferences
Traditional recommendation systems use collaborative filtering or content-based approaches in isolation, missing the rich semantic relationships between interests.
Why EKG is Required¶
Enterprise Knowledge Graphs provide sophisticated interest modeling:
- Semantic interest hierarchy — Represent interests at multiple levels (e.g., "Technology" → "AI" → "Machine Learning" → "Neural Networks")
- Interest relationships — Model how interests relate to each other (complementary, similar, opposite)
- Multi-source integration — Combine signals from purchases, content consumption, social media, search, and explicit preferences
- Temporal evolution — Track how interests emerge, grow, and fade over time
- Cross-domain inference — Infer interests from indirect signals using semantic reasoning
- Personalization at scale — Generate personalized experiences based on rich interest understanding
- Explainable recommendations — Provide transparent explanations for why content or products are recommended
Business Value¶
- Hyper-personalization — Deliver highly relevant content, products, and experiences based on deep interest understanding
- Content recommendations — Power recommendation engines with semantic interest matching
- Targeted marketing — Reach customers with messages aligned to their specific interests
- Product development — Identify emerging interests and unmet needs for new product opportunities
- Customer segmentation — Create interest-based segments beyond traditional demographics
- Cross-sell optimization — Recommend complementary products based on interest relationships
- Engagement optimization — Increase engagement by matching content to evolving interests
Common Interest Categories¶
Organizations typically model interests across multiple domains:
- Content topics — News, entertainment, sports, politics, technology
- Product categories — Fashion, electronics, home improvement, automotive
- Hobbies and activities — Travel, cooking, fitness, gaming, photography
- Brands and companies — Specific brands customers follow or prefer
- Professional interests — Industry topics, technologies, methodologies
- Life stages and goals — Home buying, retirement planning, education
Comparison with Social Graph¶
The Interest Graph focuses on what people care about — relationships between people and topics. The Social Graph focuses on who knows whom — relationships between people. Combining both graphs provides powerful insights: understanding what your customers' friends and influencers care about can predict emerging interests.
Related Use Cases¶
- Recommendation Engine - Uses interest graphs as a key component for generating personalized recommendations