Throughout last year I had the opportunity to participate and collaborate on multiple research initiatives in the field of Natural Language Generation (NLG) in addition to my responsibilities as a Data Scientist at trivago. NLG is the process of automatically generating text from either text and/or non-linguistic data inputs. Some NLG applications include chatbots, image captioning, and report generation. These are application areas of high interest internally within trivago as we seek to leverage our rich data environment to enrich the user experience with potential NLG applications.
Open Source at trivago
Insights, experiences and learnings from trivago's tech teams.
At trivago we operate on petabytes of data. In live-traffic applications that are related to the bidding business cases we use our in-house in-memory key-value storage-service written in Java to keep data as close to calculation logic as possible.
Filtering is an important way to find what you're really looking for, so why should we be okay with some users not being able to access them? We're not, so we did something about it.
Concepts like separation of concerns, logic decoupling or dependency injection are things we developers have heard more than a couple of times. At trivago, the Android app is developed using the Model View ViewModel (MVVM) architecture, aiming for views as dumb as possible, leaving the decision making to the view models. This leads to an increased test coverage since testing logic in views is something we can’t do that easily.