Learning to rank is a technique used by all the big search engines (Google, Bing, Yandex, etc.) that allows you to apply a machine-learning model in the construction of a ranking model for information retrieval systems. In fact, it is well known that sophisticated models can make more nuanced ranking decisions than a traditional ranking function. At the moment, there is not an open source solution available, but Bloomberg is working on implementing an open source plugin for Solr (an open source search engine), enabling others to easily build their own learning-to-rank systems and access the rich matching features readily available in Solr. Diego Ceccarelli presents learning-to-rank key concepts and explains how the Solr plugin works.
Read full article from Open sourcing learning to rank in Solr: O'Reilly Open Source Convention: OSCON, October 17 - 20, 2016 in London, UK
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