Subject
10 entries
Recommendation Systems
Bookmarks
Monolith: Real Time Recommendation System With Collisionless Embedding Table
Monolith is ByteDance's production recommendation system purpose-built for online training with dynamic sparse features, featuring a collisionless embedding table with expirable embeddings and frequency filtering. It demonstrates that general-purpose ML frameworks like TensorFlow are ill-suited for industrial recommendation with non-stationary data distributions.
An Empirical Investigation of Personalization Factors on TikTok
First empirical audit of TikTok's recommendation algorithm using sock-puppet methodology, testing how language, location, follows, likes, and watch duration shape content recommendations. Follow-behavior is the dominant personalization signal, and all tested factors contribute to filter bubble formation.
Collaborative Filtering Doesn't Work for Us
Chatroulette's engineering post explaining why traditional collaborative filtering didn't work for their video chat matching problem — no persistent user history, no item catalog, and the need for real-time matching under hard constraints. A useful case study on where standard recommender patterns break down.
10 Categories of Deep Recommendation Systems
James Le's survey of 10 categories of deep learning-based recommendation systems — from MLP and autoencoder approaches through attention-based and graph neural network methods. A useful taxonomy for understanding how the field moved beyond matrix factorization.
TikTok and the Sorting Hat
Eugene Wei's landmark essay arguing TikTok's algorithm is the product — not the content or creators — and that it functions as a 'sorting hat' that matches users to their taste tribes faster than any previous social network. Required reading for anyone thinking about content recommendation systems.
Building the Next New York Times Recommendation Engine
The NYT engineering blog's post on building their new recommendation engine — combining collaborative filtering with content-based signals to recommend articles. A rare look at production recommendation systems at a major media company before the algorithmic feed era fully arrived.
Implicit Feedback and Collaborative Filtering
A technical post on implicit feedback in collaborative filtering — when you have views, clicks, and dwell time instead of explicit ratings. Covers the ALS (alternating least squares) approach and linear algebra tricks for making matrix factorization tractable at scale.
Cloudera Rebuilding Machine Learning for Hadoop with Oryx
GigaOm's coverage of Cloudera launching Oryx — an open-source ML-on-Hadoop framework using the Lambda Architecture for batch retraining plus real-time serving. An early attempt to make production machine learning first-class on Hadoop.
Why Recommendation Engines Are About to Get Much Better
Coverage of advances in recommendation engine technology in 2013 — driven by larger datasets, better collaborative filtering, and contextual signals. The moment when personalization was transitioning from a luxury to an expectation.
Recommender Systems
Wikipedia's overview of recommender systems — collaborative filtering, content-based filtering, and hybrid approaches. Saved in 2012 when Netflix Prize aftermath and the rise of personalization made recommendation algorithms a hot research area.