How Bloomberg Integrated Learning-to-Rank into Apache Solr

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How Bloomberg Integrated Learning-to-Rank into Apache Solr

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Thursday, February 9, 2017 - 3:25pm

CAMPAIGN: Bloomberg: Philanthropy & Engagement

CONTENT: Blog

The latest milestone in open source development at Bloomberg is the incorporation of the Learning-to-Rank (LTR) plug-in into Apache Solr 6.4.0, which shipped this week. The release of the plug-in marks 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.

Apache Solr is an open source enterprise search platform built on top of the Apache Lucene search engine library. Solr powers search for companies and websites worldwide, and it is one of several open source projects to which Bloomberg engineers contribute as part of the company’s ongoing effort to actively participate in the open source community.

Development of the Solr Learning-to-Rank plug-in was jointly led by Michael Nilsson and Joshua Pantony from the Unified Search team in New York and Diego Ceccarelli from the News Search team in London. Individual code contributors included New York-based Jon Dorando, Naveen Santhapuri and David Grohmann from Bloomberg, and, from outside Bloomberg, London-based Alessandro Benedetti (Benedetti and Ceccarelli had initially met since they were both Italian and working on Solr in London).

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Keywords: Innovation & Technology | Bloomberg | London | New York | Research | Responsible Business & Employee Engagement | data | solr | tech

CAMPAIGN: Bloomberg: Philanthropy & Engagement

CONTENT: Blog