The History and Future of Machine Learning at Reddit

Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, including the pitfalls and mistakes made as well as their current ML projects and future efforts in the space. Based on a talk given by Anand Mariappan, the Senior Director of ML at Reddit, at ODSC West 2018, we’ll cover data platform, ML efforts around anti-evil, abuse, feed ranking, recommendations, user and subreddit similarity, as well as business results achieved and how they measure those through our experiments systems.

[Related Article: IBM Research Launches Explainable AI Toolkit]

First, What is Reddit?

Reddit, at its core, is a social network of communities that helps users find people with similar passions and connect around those ideas. It’s designed to allow open interaction and shared information, without the other pressures of modern social media. It has incredible engagement and is a bigger platform than even users know it to be.

This is all achieved through four different features.

Pseudonymous: By letting users create profiles without their real information, Reddit has allowed them to have honest conversations that they may not be able to have in their real life. While there have been some issues surrounding people bullying because they don’t perceive repercussions, it’s been an overwhelmingly positive feature that is key to Reddits user interface.

Community and Rules: Part of the solution to anonymous bullying is the idea of communities and rules. To participate in a Reddit thread, you join communities and have to follow the posted community rules. If a user breaks the rules, either by bullying or by spamming or self promotion or whatever other rule they may have, the moderators are able to remove a user.

Voting: Voting is also an integral part of the Reddit space, which both encourages genuine quality content, and helps the Reddit team recommend and track content to other users who may be interested in it.