Bloomberg Terminal: Making business smarter with machine learning - Computer Business Review

Earlier this year, Bloomberg reached a milestone in open source development with the incorporation of the Learning-to-Rank plug-in into Apache Solr 6.4.0. The release of the plug-in was the culmination of a year's worth of close collaboration between two groups of Bloomberg software engineers in New York and London and the open source project's community to make it easier to re-rank search results using machine learning. In an exclusive Q&A with Computer Business Review's James Nunns, software engineers and project collaborators Diego Ceccarelli, Michael Nilsson and Christine Poerschke at Bloomberg (who also served as the Apache Lucene/Solr committer in this process) shared insights about their experience, challenges and learnings. Diego Ceccarelli: "Our project was intended to add Learning-to-Rank (LTR) functionality to open source enterprise search platform Apache Solr in order to improve both Federated Search and News Search on the Bloomberg Terminal. LTR is a technique for improving the relevance and performance of search that was proposed in academia more than 10 years ago. Today, several major commercial search engines use this technique but, although there is some software written to extend it on the web, we realized it didn't exist inside Solr, which we use to power search across a number of Terminal functions."