# Engineering stories behind the Medium Daily Digest Algorithm: Part 4

DevFeed: [Engineering stories behind the Medium Daily Digest Algorithm: Part 4](<https://devfeed.tech/articles/engineering-stories-behind-the-medium-daily-digest-algorithm-part-4-20319.md>)

Original publisher: [Read original article](<https://medium.engineering/engineering-stories-behind-the-medium-daily-digest-algorithm-part-4-ec7136f21acd?source=rss----2817475205d3---4>)

Author: Raphael Montaud

Published: 2025-08-25T18:31:30Z

Content type: article

Language: en

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

Topics: [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [daily](<https://devfeed.tech/tags/daily.md>), [database](<https://devfeed.tech/tags/database.md>), [improvements](<https://devfeed.tech/tags/improvements.md>), [issue](<https://devfeed.tech/tags/issue.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [programming](<https://devfeed.tech/tags/programming.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [technical](<https://devfeed.tech/tags/technical.md>)

## AI overview

This engineering article examines how Medium investigated repetitive Daily Digest recommendations reported by power users and made incremental improvements to the underlying algorithm. The series reports that the work improved conversion to paying memberships by 10%, reduced operating costs, and made the system more flexible and maintainable.

## Source excerpt

Cross-Digest diversification In this part 4, we'll see how we went from investigating a few complaints from digest power users to improving our digest recommendations across the board. 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 this story You may need this to understand the rest of this postUser Complaints After we shipped all the changes mentioned in the previous installments of this series, we started seeing some support tickets coming in related to the digest: User complaints that started off our investigation I think we should appreciate the level of thoughtfulness our users put in those support tickets. We review those carefully and we take pride in reading and answering every support tickets. Those were forwarded to the recommendation team and we immediately thought that maybe we oversteered to much when we removed some of the filtering rules for the digest. User digests were too repet