trivago Intelligence was born in 2013 with two main objectives: First, to provide bidding capability to the advertisers, who are listed on trivago, and second, to provide them with metrics related to their own hotels; like clicks, revenue, and bookings (typical BI data). This project faced a wave of inevitable data growth which lead to a refactoring process which produced a lot of learnings for the team. As I expect it to be useful for other teams who deal with similar challenges, this article will describe why a team started a full migration of technologies, how we did it and the result of it.
Posts about Data Science
Introduction As a user researcher, it is important to know more about our users and their preferences concerning our product. One way to do that is by conducting surveys. In order to gather user feedback from our global markets, we need to conduct a survey with a slightly different set of questions/translations for different countries, and then analyze the results and compare if there is any difference across countries concerning user needs.
While searching for “Spa and Wellness hotels in Berlin…” I land on trivago. Surprisingly the main images of the hotels exactly reflect the spa concept that I am searching for. It helped me better compare hotels on the list for finding my ideal accommodation for my vacation! This was the user experience we were looking for when we kicked off the Image Concepts project at trivago. The users with clear hotel search intent who are looking for a specific concept hotel before coming to trivago are redirected to the landing pages related to that particular topic.
Our data scientists and engineers love the challenges that their work presents to them on a daily basis and thrive in our agile environment where they can share their knowledge, learn from others, and work together to solve any problems that arise. We are always looking for ways to share the unique problem settings we encounter and to inspire a productive exchange on algorithm development and evaluation. That is why we have partnered with researchers from TU Wien, Politecnico di Milano, and Karlsruhe Institute of Technology to launch the RecSys Challenge 2019, the annual data science challenge of the ACM Recommender Systems conference.
When faced with the challenge to store, retrieve and process small or large amounts of data, structured query languages are typically not far away. These languages serve as a nice abstraction between the goal that is to be achieved and how it is actually done. The list of successful applications of this extra layer is long. MySQL users could switch from MyISAM to InnoDB or use new algorithms like Multi-Range-Read without a change to their application.
Machine Learning (ML) engineering and software development are both fundamentally about writing correct and robust algorithms. In ML engineering we have the extra difficulty of ensuring mathematical correctness and avoiding propagation of round-off errors in the calculations when working with floating-point representations of a number. As such, ML engineering and software development share many challenges… and some of the solutions to these. Concepts like unit testing and continuous integration rapidly found its way into the jargon and the toolset commonly used by data scientist and numerical scientist working on ML engineering.
Tackling hard problems is like going on an adventure. Solving a technical challenge feels like finding a hidden treasure. Want to go treasure hunting with us?View all current job openings