Search at Slack

This post details the development of a new personalized relevance sort for Slack search and the 'Top Results' module. It describes the unique challenges of search in Slack compared to web search, the advantages Slack has (user interaction history, no spam, controlled document structure), and the limitations of the existing 'Relevant' search. The post explains the two-stage approach to learning to rank: using Solr for initial retrieval and application layer re-ranking. It details the pairwise transform labeling strategy, overcoming click-position bias by oversampling lower-ranked results, and the key features used in the trained SVM model (message age, Lucene score, user affinity, channel priority, author affinity, message characteristics). The release to 50% of users showed a 9% increase in clicked searches and a 27% increase in clicks at position 1. The 'Top Results' module, showing the top 3 relevant results above recent results, further improved search sessions per user and reduced searches per session.