When was the last time you booked accommodation without checking its photos? Most probably never! Because having imagery information makes our decision-making process much easier and faster. However, picking up the best possible images of a hotel to show to the user is an interesting problem to solve, because it can be a naive random selection or a sophisticated machine learning model to know what the user truly wants at that moment.
Posts about Engineering
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.
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.
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