Bloomberg Adds Machine-Learning To Apache Solr Search


Bloomberg has found the Solr open source search engine valuable as a core element of several Bloomberg products. Now it has come up with a Solr plug-in that lets Bloomberg customers build a machine learning model of what information is most valuable to them, then add that information through the plug-in to Solr searches.

Since Bloomberg is a major distributor of financial news and the supplier of the frequently-used Bloomberg Terminal into which business news is streamed to the financial services industry, having an effective search engine available is part of maintaining its competitive advantage.

Giving customers the means to train that engine to rank search results according to criteria decided by the customer gives Bloomberg an ongoing advantage over other news and terminal services suppliers. Bloomberg has done that through its Learning-To-Rank plug-in for Solr. It has also made the code for the plug-in open source through the Apache Software Foundation, host to the Solr search engine project.

“We have smart people working on Solr,” said Kevin Fleming, member of the CTO’s office and head of the open source community at Bloomberg, in an interview. “You want to be a good community citizen, you want to contribute code and fix bugs. You also want to make good business decisions,” he explained on Feb. 13, as he prepared to address a session at the Linux Foundation’s Open Source Leadership Summit at Lake Tahoe Feb. 14-16.

Making the code open source assures Bloomberg customers that its ongoing development will continue at a rapid pace through an independent governing board and outside contributors, Fleming explained. It will continue even if Bloomberg’s interest in the project flags. Solr and Learning-To-Rank will remain available as open source, even if Bloomberg were, for some reason, to drop its involvement.

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