Hour 1: Recommendation Engine Basics
Recommendation Engine Basics
A recommendation engine is an AI system that suggests products, services, or content to users based on their preferences, behavior, and patterns. These engines personalize user experiences and drive engagement, sales, and retention.
🔹 Types of Recommendation Engines
- Collaborative Filtering (User Behavior–Based)
- Works by analyzing user-item interactions.
- Finds patterns in what similar users liked or purchased.
- Example:
- If User A likes products X and Y, and User B likes product X, the system recommends Y to User B.
- Used by: Amazon (suggesting “Customers who bought this also bought…”).
- Content-Based Filtering (Item Feature–Based)
- Focuses on the attributes of items and a user’s past interactions.
- Recommends items similar to what a user has liked before.
- Example:
- If you watched an action movie, the system recommends another action movie with similar actors, genre, or keywords.
- Used by: Netflix (showing movies/series based on your watch history).
- Hybrid Models (Combination of Both)
- Merges collaborative and content-based filtering.
- Provides better accuracy and avoids limitations like “cold start” (lack of data for new users/items).
- Example:
- Fiverr → suggests gigs based on your browsing (content-based) and what similar clients hired (collaborative).
🚀 Real-World Examples
- Amazon → Uses collaborative filtering to recommend products (“Frequently bought together”) and content-based for product features.
- Netflix → Uses hybrid models with user behavior + movie metadata to keep engagement high.
- Fiverr → Suggests freelancers/gigs by analyzing both gig details (skills, tags) and what similar buyers purchased.