February Meetup - Learning to Rank with Solr & Hibernate and Lucene

Feb 22, 2016 · Greater London, United Kingdom

Our first talk is on Learning to Rank with Solr from Diego Ceccarelli of Bloomberg. Learning To Rank is a technique that allows you to use a machine learnt model for ranking documents returned by a search service in response to a query. For performance reasons, usually only the top X documents retrieved using a standard ranking function (e.g. TF-IDF or BM25) are re-ranked. The hope is that sophisticated models can make more nuanced ranking decisions than a traditional ranking function. At Bloomberg we have 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 talk we will describe in detail how the plugin works, in particular how it allows feature engineering, feature extraction, and reranking.

Diego is a Software Engineer at Bloomberg LP, working in the News Search R&D team. His work focuses on improving search relevance for financial news. 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.

Our second talk is on Hibernate Search from Sanne Grinovero. Sanne is a Principal Software Engineer at Red Hat working full-time on Java OSS projects, mainly the popular Hibernate project and integrations with Apache Lucene. He leads the Hibernate Search project and is passionate about performance, concurrency, open source and scalable data related problems; especially search engines.

Hibernate Search is a popular extension of the Hibernate ORM library, which integrates Apache Lucene's indexing and search capabilities into the traditional JPA domain model. Its goal is to simplify integration to keep the full-text index in synch with database changes as directed by your container’s transactions and to transparently manage the indexing resources, implementing the most efficient patterns. This allows you to forget about the complexities of keeping multiple stateful representations in synch consistently, and allows you to focus on the fun and creative aspect of picking the advanced linguistic transformations and indexing options to deliver responsive and effective suggestions to natural language queries. During this presentation we will see a simple example and how it integrates with other OSS projects in the Java data space, such as Hibernate OGM and Infinispan, and how to use the full-text capabilities of Lucene in a JEE application running on WildFly.

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