Scenario You’re working as a search engineer for XYZ Ltd, a company which sells electric components. XYZ provided you the application logs of the last six months, and some business requirements. Two kinds of customers, two kinds of requirements, two kinds of search The log analysis shows that XYZ has mainly two kinds of customers:…
Quantity detection? What is a quantity? And why do we need to detect it? A quantity, as described by Martin Fowler in his “Analysis Patterns” [1] is defined as a pair which combines an amount and unit (such as 30 litres, 0.25 cl, or 140 cm). In search-based applications, there are many cases where you may…
This blog is a quick summary of my (subjective) experience at ECIR 2018 : the 40th European Conference on Information Retrieval, hosted in Grenoble (France) from 26/03/2018 to 29/03/2018. Deep Learning and Explicability Eight long papers accepted were about Deep Learning. The topics “Neural Network” and “Word Embedding” were the most occurring in the accepted…
Last Stage Of The Journey This blog post is about the Apache Solr Learning To Rank ( LTR ) integration. We modelled our dataset, we collected the data and refined it in Part 1 . Trained the model in Part 2 . Analysed and evaluate the model and training set in Part 3 . We…
Apache Solr Learning to Rank – Things Get Serious This blog post is about the Apache Solr Learning to Rank Tools : a set of tools to ease the utilisation of the Apache Solr Learning To Rank integration. The model has been trained in Part 2, we are ready to deploy it to Solr, but…
Model Training For Apache Solr Learning To Rank If you want to train a model for Apache Solr Learning To Rank , you are in the right place. This blog post is about the model training phase for the Apache Solr Learning To Rank integration. We modelled our dataset, we collected the data and refined it…
Learning To Rank In Apache Solr Introduction This blog post is about the journey necessary to bring Learning To Rank In Apache Solr search engines. Learning to Rank[1] is the application of Machine Learning in the construction of ranking models for Information Retrieval systems. Introducing supervised learning from user behaviour and signals can improve the relevancy…
Introduction This blog post is about the Lucene Inverted Index and how Apache Solr internally works. When playing with Solr systems, understanding and properly configuring the underline Lucene Index is fundamental to deeply control your search. With a better knowledge of how the index looks like and how each component is used, you…
This blog post is about the Apache Solr Autocomplete feature. It is clear that the current documentation available on the wiki is not enough to fully understand the Solr Suggester : this blog post will describe all the available implementations with examples and tricks and tips. Introduction If there’s one thing that months of Solr-user…
Introduction This blog post is about the Solr classification module and the way Lucene classification has been integrated at indexing time. In the previous blog [1] we have explored the world of Lucene Classification and the extension to use it for Document Classification . It comes natural to integrate Solr with the Classification module and…