Subject
9 entries
Collaborative Filtering
Bookmarks
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.
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 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.
How a Math Genius Hacked OkCupid to Find True Love
Chris McKinlay scraped OkCupid, clustered female users with k-means, and optimized his profile to score high compatibility across all clusters — then met his wife through the resulting message flood. A crowd-pleasing 2014 story about data science applied to dating.
Introduction to Recommendations with Map-Reduce and mrjob
Tutorial on building item-based collaborative filtering recommendation systems using MapReduce and Yelp's mrjob Python library. Shows why distributed computation is necessary for large-scale similarity calculations.
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.
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.
How to Get Hilary Mason to Build Your Recommender for Free
Mortar Data's post on building a free recommender system using Hilary Mason's approach — a practical guide to collaborative filtering on Hadoop using Mahout. A snapshot of the state of accessible recommendation infrastructure in 2013.