This report explores the New York Times’ recommendation engine, based on the article Building the Next New York Times Recommendation Engine by Alexander Spangher. The goal is to understand how the system works, who it serves, and how it might be improved. We’ll use a technique called Scenario Design, which helps us think through how different users interact with the system and what they need from it.
Scenario Design is a way to imagine how people use a product or system. It asks three simple questions:
We’ll apply this twice: once for the New York Times as an organization, and once for its readers.
Based on the article and site behavior, the NYT recommendation engine likely uses:
The system also seems to use Bayesian models, neural networks, and dimensionality reduction to better understand user preferences and article features.
Improve cold-start handling: New users or new articles may not get good recommendations. Consider using demographic or contextual data (e.g., location, time of day) to improve early suggestions.
Increase transparency: Let users know why something is recommended. A simple “Because you read X” can build trust.
Let users customize: Allow readers to follow topics, authors, or formats (e.g., “more podcasts, fewer opinion pieces”).
Balance personalization with serendipity: Mix in surprising or diverse content to avoid filter bubbles.
Use feedback loops: Let users thumbs-up/down recommendations to improve future suggestions.
Test for bias: Regularly audit the system to ensure it doesn’t reinforce stereotypes or political echo chambers.
The New York Times recommendation engine is a powerful tool for connecting readers with relevant journalism. By designing with both the organization’s and readers’ goals in mind, and by making the system more transparent and adaptive, the NYT can continue to lead in digital news personalization.