Recommendation System

What problem does it solve?

Build and deploy end-to-end recommendation systems that predict user preferences and surface personalized item suggestions, improving engagement and conversions.

Core Features & Use Cases

• Collaborative Filtering: Recommend items to users based on similar users' behavior.
• Content-based: Recommend items using item attributes and user preferences.
• Hybrid: Combine collaborative and content-based signals for better accuracy.
• Matrix Factorization & Deep Learning: Learn latent representations to reveal hidden patterns in user-item data.
• Production-ready evaluation: Use metrics like Precision@K, Recall@K, NDCG, coverage, and diversity; run A/B tests to measure business impact.

Quick Start

Train a small-scale, end-to-end pipeline to build a user-item matrix, compute similarity or latent factors, generate top-N recommendations for a sample user, and evaluate results.