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.
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.
As we all adventure around this space that we call the Internet, consuming content is often on our minds. Naturally with the vast amount of data, filtering out what’s not interesting is a huge time saver. In order to help you find your ideal hotel at the best price, trivago’s filters are one of the best ways to do so. Sadly, some visitors couldn’t even access them due to poor accessibility and performance.
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.