# Engineering stories behind the Medium Daily Digest Algorithm: Part 1

DevFeed: [Engineering stories behind the Medium Daily Digest Algorithm: Part 1](<https://devfeed.tech/articles/engineering-stories-behind-the-medium-daily-digest-algorithm-part-1-20317.md>)

Original publisher: [Read original article](<https://medium.engineering/engineering-stories-behind-the-medium-daily-digest-algorithm-part-1-909a7ca5e807?source=rss----2817475205d3---4>)

Author: Raphael Montaud

Published: 2025-08-26T11:31:37Z

Content type: article

Language: en

Sources: [Medium](<https://devfeed.tech/sources/medium.md>)

Topics: [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [model](<https://devfeed.tech/tags/model.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [technical](<https://devfeed.tech/tags/technical.md>)

## AI overview

This engineering article begins a four-part series about improving Medium's Daily Digest recommendation algorithm. It describes a mismatch between recommendations in the Digest and the homepage feed, despite both using the same algorithm, model, and features, and introduces an investigation into differences in filtering.

## Source excerpt

How we made our email story recommendations better In this Part 1, you'll understand how we improved one of the main ways our users are exposed to our product and how that led to a massive 7% increase on the average reading time for the digest users. Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium's Daily Digest over the past year. When we started this work, the Digest was suboptimal -- and since it's a huge distribution surface, reaching millions of readers every day, we started working on incremental improvements.By the end of these projects, the digest was 10% more likely to convert users to paying members, less expensive to run, more flexible and easier to maintain and it's now providing higher quality recommendations for all our users, including our "power readers".This is told through the lens of our engineering team tackling a series of challenges one by one. Medium has a small team but we operate on a big scale. We're working our way through some technical debt and at the same time, striving to provide the best experience for our readers. This is the source of many interesting challenges.I hope this series helps you understand how the recommendations algorithm work and can help others who are facing similar technical challenges.Some Concepts Here's a little cheat sheet with some concepts you may need to follow along with this story You may need this to understand the rest of this postThe Discovery A little while back, Leigh, our Machine Learning Engineer and model training guru, started noticing something weird. The recommended stories in his digest were consistently not great matches for his reading profile. At the same time, the recommended stories in his homepage feed (what we call the "For You" feed) were consistently very well targeted. This was a bit puzzling and unexpected. Those two recommendation surfaces rely on exactly the same algorithm. We source stories the same way, and we rank them using the same mo