The Goal: Deliver a personalized, high-engagement stream of content that predicts what a user wants to see next.
Technical Implementation: Implement a Hybrid Recommender System. Use Collaborative Filtering (finding users with similar trip histories) combined with Content-based Filtering (matching trip tags to user preferences). use Gen Ai leverage Vector Embeddings stored in a database like Pinecone or Milvus to perform fast similarity searches on trip metadata.
What is Not Accepted: A static, chronological "latest first" feed. Hardcoded "featured" items that are the same for everyone. Synchronous processing that slows down the API (calculate feeds asynchronously and cache in Redis).
The Goal: Deliver a personalized, high-engagement stream of content that predicts what a user wants to see next.
Technical Implementation: Implement a Hybrid Recommender System. Use Collaborative Filtering (finding users with similar trip histories) combined with Content-based Filtering (matching trip tags to user preferences). use Gen Ai leverage Vector Embeddings stored in a database like Pinecone or Milvus to perform fast similarity searches on trip metadata.
What is Not Accepted: A static, chronological "latest first" feed. Hardcoded "featured" items that are the same for everyone. Synchronous processing that slows down the API (calculate feeds asynchronously and cache in Redis).