Bringing personalisation to data discovery, Learning to Rank 101
15:10/15:50
Traditional search engine approaches for providing the most relevant results for a query has been focus on matching the query terms with the words in the documents, mainly TF-IDF and BM25, however with methods are hard to tune for best results and provide no personalisation. This talk will introduce Learning to Rank, a machine learning approach to bring personalisation to search, and it’s key concepts, before diving into a real life demo based on elasticsearch and real data. At the end of it you will take home a basic understanding of LTR, applications and enough to start using it.
Language: English
Level: Intermediate
Pere Urbon-Bayes
Data and Software Engineering Consultant - Freelance
Pere has been working with data and architecting systems from more than 15 years. As an engineer and consultant freelancer he is focused on data processing and search, helping companies build reliable and scalable data architectures. His work sits at the crossroad of infrastructure, data engineers and scientist, ontologist and product. Prior to that he was part of Elastic, the company behind Elasticsearch, where he was part of the Logstash team, helping companies build reliable ingestion pipelines into Elasticsearch.
