As we countdown to the annual Lucene/Solr Revolution conference in Boston this October, we’re highlighting talks and sessions from past conferences. Today, we’re highlighting Bloomberg’s Michael Nilsson and Diego Ceccarelli’s talk, “Learning to Rank in Solr”.

In information retrieval systems, learning to rank is used to re-rank the top X retrieved documents using trained machine learning models. The hope is that sophisticated models can make more nuanced ranking decisions than a standard Solr query. Bloomberg has integrated a reranking component directly into Solr, enabling others to easily build their own learning to rank systems and access the rich matching features readily available in Solr. In this session, Michael and Diego review the internals of how Solr and Lucene score documents and present Bloomberg’s additions to Solr that enable feature engineering, feature extraction, and reranking.

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Michael Nilsson is a software engineer working at Bloomberg LP, and has been a part of the company’s Search and Discoverability team for four years. He’s used Solr to build the company’s terminal cross domain search application, searching though millions of people, companies, securities, articles, and more.

Diego Ceccarelli is a software engineer at Bloomberg LP, working in the News R&D team. His work focuses on improving search relevance in the news search functions. Before joining Bloomberg, Diego was a researcher in Information Retrieval at the National Council of Research in Italy, whilst completing his Ph.D. in the same field at the University of Pisa. He is experienced in Lucene and Solr, dating back to his work on the Europeana project in 2010, and since then enjoys diving into these technologies.

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