ML Recommender System – Collaborative Filtering

I will design and build a recommendation system for your product or content platform — using collaborative filtering (matrix factorisation with ALS or SVD), content-based filtering, or a hybrid approach — with cold-start handling, evaluation with Precision@K and NDCG metrics, and a REST API delivering ranked recommendations per user. Recommendation systems are one of the highest-ROI ML applications for e-commerce, streaming, and content platforms — but they are also one of the most technically nuanced, with cold-start problems, popularity bias, and filter bubble effects requiring deliberate design decisions.

The system covers interaction data preparation (implicit or explicit feedback), matrix factorisation training with Implicit library or Surprise, cold-start strategy (popularity baseline or content-based fallback), offline evaluation with held-out interactions, recommendation API returning top-K items per user, and a diversity or novelty configuration option for recommendation list post-processing.

Designed for e-commerce businesses, content platforms, learning management systems, and any application with a catalogue of items where personalised recommendations drive engagement and revenue.