Aug 21, 2015 · Artificial Intelligence

How Facebook Scales Recommendations with Distributed Machine Learning and Giraph

This article explains how Facebook tackles massive recommendation data—over 100 billion ratings—by using distributed collaborative filtering, matrix factorization, SGD/ALS hybrid algorithms, and a novel work‑to‑work communication scheme built on Apache Giraph to achieve high performance and scalability.

ALSApache GiraphFacebook

0 likes · 9 min read

How Facebook Scales Recommendations with Distributed Machine Learning and Giraph

Art of Distributed System Architecture Design

Aug 21, 2015 · Artificial Intelligence

Facebook’s Distributed Recommendation System: Architecture, Algorithms, and Performance

The article explains how Facebook built a large‑scale distributed recommendation system using Apache Giraph, collaborative filtering with matrix factorization, SGD and ALS algorithms, a novel work‑to‑work communication scheme, and performance optimizations that achieve ten‑fold speedups on billions of ratings.

ALSApache GiraphFacebook

0 likes · 9 min read