Subject
4 entries
Recommender Systems
Bookmarks
Exploring Production-Ready Recommender Systems with NVIDIA Merlin
NVIDIA Merlin is a framework for building GPU-accelerated production recommender systems — covering feature engineering (NVTabular), training (HugeCTR, Merlin Models), and serving (Triton). This post explores the end-to-end pipeline for large-scale recommendation.
Explicit Recommender System — Matrix Factorization in PyTorch
A tutorial implementing explicit recommender systems via matrix factorization in PyTorch — using Embedding layers for user and item factors, trained with alternating gradient descent. Concrete implementation of collaborative filtering fundamentals.
Building a Recommender System Using Embeddings
Drop Engineering's walkthrough of building a brand recommender using learned embeddings — training entity embeddings from user-brand interaction data to capture brand similarity in continuous vector space. A practical case study in embedding-based recommendations.
The State of Recommender Technology (2013)
A 2013 survey of recommender system technology covering collaborative filtering, content-based approaches, and the state of the field before deep learning took over. Published by Data Community DC alongside coverage of CoBrain, a startup working on recommendation infrastructure.