Introducing Highlights

This post details the technical implementation of Slack's 'Highlights' feature, which uses machine intelligence to predict important messages. It describes the 'work graph' concept for capturing communication relationships and the use of logistic regression models with over a hundred features to predict user engagement. The post also addresses class imbalance issues through stratified sampling and prior correction, and discusses personalization by grouping user behaviors and incorporating user-specific features from the work graph. A justification layer is also described, which uses feature weights to explain why a message is highlighted.