Rex: Medium's Go recommendation microservice for personalized story feeds

“We source stories we think a user will enjoy, and we understand users may like stories for different reasons. For example, you may always read stories from authors or publications you follow”

“Finding users with a similar reading history to mine and making recommendations based on those is a technique called collaborative filtering, which Rex relies on to find high-quality stories for each user”

“we filter out stories we think may not be suitable for a user at a given time. Maybe we've sourced a user a story they've already read — there's no need to show them the same story twice”

“We calculate most of the features we need for ranking stories via offline Scala jobs and store them in two tables that we query at the time of feed creation. This allows us to minimize the number of I/O calls we're making when assembling…”

“we pass each story and set of values to another Medium microservice that hosts our feed-ranking models. This separate microservice assigns a score to each story, where the score represents how likely we think the user is to read this par…”

“we'll see the top of a user's feed dominated at times by a single author, single publication, or single topic. Because we want a user to see a more diverse set of authors, publications, and topics represented, we added a postprocessor”

“we store the feed in Redis, an in-memory data store, for future use”

“some of the stories in the cached ranked-feed list may no longer be suitable for candidates”