Almost every day we go online we encounter recommender systems; if you are listening to your favorite song on Spotify, binge watching a TV show on Netflix or buying a new laptop on Amazon. Although we all know these recommendation engines exist, it is less known what algorithms lie behind such recommendations. To get a better understanding of the algorithms used in recommender systems we decided to build a recommender ourselves! With COVID-19 making us more housebound than ever, a topic for our recommendation engine was quickly found; we decided to build a book recommender using Python. Although there are ready-to-use packages to build recommender systems (such as Surprise) we decided to built or own recommendation system. We did this because our goal was to understand how a recommendation engine works rather than just have a book recommender. Moreover, we wanted to be able to control the specifications of the variables used in our engine and wanted to avoid the black box. In this notebook we explain step-by-step how we built our recommendation engine.

For our third Meetup about putting Artificial Intelligence (A.I.) to practice, we were very happy to welcome Thomas Stalman and Peter van Lith. They had two quite different, but very interesting stories. And despite the beautiful weather and the national strike at the regional public transport, they had a full room of people to share […]