With advanced tools available for search like Solr and Elasticsearch, companies are embedding search in almost all their products and websites. Search is becoming mainstream. Therefore we can focus on teaching the search engine tricks to return more relevant results. One new trick is called “learning to rank”. Learning to rank uses a trained model to come up with a better ranking of the search results. During the presentation you’ll learn what Learning To Rank is. To be able to understand the machine learning part, you get information about machine learning models, feature extraction and the training of models. You will also learn about when to apply learning to rank and of course you’ll get an example to show how it works using elasticsearch and a learning to rank plugin. After this presentation you have learned how and why to combine Machine Learning and Search.
Fellow at Luminis /Amsterdam
Jettro is a Fellow at Luminis in Amsterdam, specialized in Search. He has a strong background in the Java eco system. He also has experience in the front-end with React and Angular. These days Jettro is mainly involved with the elastic stack and he is learning about and experimenting with Machine Learning. Combining Machine Learning with Search to solve one of the hardest search problems, named ranking, is what keeps him going. Jettro believes in the vision of Luminis that: Knowledge is the only thing that increases by sharing. Therefore he writes blogs, gives trainings and presents at a number of meetups and conferences.