Vector Databases
1Vector Databases for Recommendation Engines: Episode Notes
Introduction
• Vector databases power modern recommendation systems by finding relationships between entities in high-dimensional space
• Unlike traditional databases that rely on exact matching, vector DBs excel at finding similar items
• Core application: discovering hidden relationships between products, content, or users to drive engagementKey Technical Concepts
Vector/Embedding: Numerical array that represents an entity in n-dimensional space
• Example: [0.2, 0.5, -0.1, 0.8] where each dimension represents a feature
• Similar entities have vectors that are close to each other mathematically
Similarity Metrics:
• Cosine Similarity: Measures angle between vectors (-1 to 1)
• Efficient computation: dot_product / (magnitude_a * magnitude_b)
• Intuitively: measures alignment regardless of vector magnitude
Search Algorithms:
• Exact Nearest Neighbor: Find K closest vectors (computationally expensive)
• Approximate Nearest Neighbor (ANN): Trades perfect accuracy for speed
• Computational complexity reduction: O(n) → O(log n) with specialized indexingThe "Five Whys" of Vector Databases
Traditional databases can't find "similar" items
• Relational DBs excel at WHERE category = 'shoes'
• Can't efficiently answer "What's similar to this product?"
• Vector similarity enables fuzzy matching beyond exact attributes
Modern ML represents meaning as vectors
• Language models encode semantics in vector space
• Mathematical operations on vectors reveal hidden relationships
• Domain-specific features emerge from high-dimensional representations
Computation costs explode at scale
• Computing similarity across millions of products is compute-intensive
• Specialized indexing structures dramatically reduce computational complexity
• Vector DBs optimize specifically for high-dimensional similarity operations
Better recommendations drive business metrics
• Major e-commerce platforms attribute ~35% of revenue to recommendation engines
• Media platforms: 75%+ of content consumption comes from recommendations
• Small improvements in relevance directly impact bottom line
Continuous learning creates compounding advantage
• Each customer interaction refines the recommendation model
• Vector-based systems adapt without complete retraining
• Data advantages compound over timeRecommendation Patterns
Content-Based Recommendations
• "Similar to what you're viewing now"
• Based purely on item feature vectors
• Key advantage: works with zero user history (solves cold start)
Collaborative Filtering via Vectors
• "Users like you also enjoyed..."
• User preference vectors derived from interaction history
• Item vectors derived from which users interact with them
Hybrid Approaches
• Combine content and collaborative signals
• Example: Item vectors + recency weighting + popularity bias
• Balance relevance with exploration for discoveryImplementation Considerations
Memory vs. Disk Tradeoffs
• In-memory for fastest performance (sub-millisecond latency)
• On-disk for larger vector collections
• Hybrid approaches for optimal performance/scale balance
Scaling Thresholds
• Exact search viable to ~100K vectors
• Approximate algorithms necessary beyond that threshold
• Distributed approaches for internet-scale applications
Emerging Technologies
• Rust-based vector databases (Qdrant) for performance-critical applications
• WebAssembly deployment for edge computing scenarios
• Specialized hardware acceleration (SIMD instructions)Business Impact
E-commerce Applications
• Product recommendations drive 20-30% increase in cart size
• "Similar items" implementation with vector similarity
• Cross-category discovery through latent feature relationships
Content Platforms
• Increased engagement through personalized content discovery
• Reduced bounce rates with relevant recommendations
• Balanced exploration/exploitation for long-term engagement
Social Networks
• User similarity for community building and engagement
• Content discovery through user clustering
• Following recommendations based on interaction patternsTechnical Implementation
Core Operations
• insert(id, vector): Add entity vectors to database
• search_similar(query_vector, limit): Find K nearest neighbors
• batch_insert(vectors): Efficiently add multiple vectors
Similarity Computation
• fn cosine_similarity(a: &[f32], b: &[f32]) -＞ f32 {
let dot_product: f32 = a.iter().zip(b.iter()).map(|(x, y)| x * y).sum();
let mag_a: f32 = a.iter().map(|x| x * x).sum::
Integration Touchpoints
• Embedding pipeline: Convert raw data to vectors
• Recommendation API: Query for similar items
• Feedback loop: Capture interactions to improve modelPractical Advice
Start Simple
• Begin with in-memory vector database...