Finding Money in the Haystack: Information Retrieval at Bloomberg

Jonathan J. Dorando, Konstantine Arkoudas, Parth Vasa, Gary Kazantsev, Gideon Mann

Abstract: The financial markets are a rich domain for search, and it is not simple to serving the entire scope of financial professionals, who make their living on accurate, timely, and deep information. The data sources are many and disparate. This includes domains with rich structured data such as company and security attributes, textual data like research reports, and time sensitive news stories. Not only is the domain complicated, but some of the techniques that work for web search have to be adapted and reconsidered in an enterprise context with fewer eyeballs but just as complicated questions. At Bloomberg, we have been addressing these problems over the past four years in the search and discoverability group, heavily leveraging the insights from the academic and open-source communities to apply to our problems. We'll discuss about our efforts in Natural Language Question & Answer (NLQA), learning to rank, federated search, crowd sourcing, and how this all comes together to make search effective for our users.