The Challenge
E-commerce and content platforms need to deliver personalized recommendations in real-time, considering millions of users, products, and complex interaction patterns. Traditional collaborative filtering approaches struggle with:
- Cold start problem for new users and items
- Real-time personalization at scale
- Balancing exploration vs exploitation
- Multi-dimensional similarity (collaborative + content-based)
- Context-aware recommendations
ArcadeDB Advantage
Combine graph traversal for collaborative filtering with vector similarity search for content-based recommendations:
- Graph Relationships: User-product interactions, social connections, co-purchase patterns
- Vector Embeddings: Product features, user preferences, semantic similarity
- Real-time Personalization: Generate recommendations in <20ms
- Hybrid Approach: Best of collaborative + content-based in one query