Kim Falk
Principal Recommender Engineer at DPG Media België
Antwerp, Antwerp, Belgium
Summary
🤩
Rockstar🎓
Top SchoolKim Falk is a Principal Recommender Engineer with 11+ years building and shipping production-grade recommender systems across e-commerce, media and legal tech, most recently leading strategy and delivery for product recommendations at Shopify and now at DPG Media België. He combines deep technical hands‑on experience—from graph neural networks and TensorFlow pipelines to item2vec and reinforcement learning—with mentoring and technical leadership that drives teams toward state-of-the-art personalization. Author of Practical Recommender Systems and a persistent open-source practitioner (notably the Django-based moviegeek example used in his book), he blends clear pedagogy with production pragmatism. He’s advised companies like IKEA, RTL and Storytel on evaluation and strategy, and has built Danish NLP and legal verdict classifiers, a detail that highlights his ability to apply recommender thinking beyond retail into language and high-stakes domains.
11 years of coding experience
21 years of employment as a software developer
M.Sc. Master of Science Computer Science; Information Technology, M.Sc. Master of Science Computer Science; Information Technology at Aarhus UniversityStatistical Method in Machine Learning, Statistical Method in Machine Learning at Københavns Universitet - University of CopenhagenNanodegree Deep Reinforcement Learning, Nanodegree Deep Reinforcement Learning at UdacityStud. Mat-fys, Stud. Mat-fys at Aarhus Katedralskole
English, Danish, Italian
Github Skills (7)
python10
recommender-system10
django10
data-science9
sql8
pandas7
machine-learning6
Programming languages (3)
Github contributions (5)
practical-recommender-systems/moviegeek
May 2016 - Feb 2022
A django website used in the book Practical Recommender Systems to illustrate how recommender algorithms can be implemented.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:30 commits, 14 PRs, 215 pushes in 5 years 9 months
Contributions summary:Kim primarily contributed to the backend logic of the Django-based recommender system. Their commits included refactoring code for readability, updating analytics charts and views, and modifying database models. The user also implemented improvements related to data processing and model building, particularly within the context of a content-based recommendation algorithm. They made adjustments to the code related to the application's features.
django-websitepythonrecommenderdata-sciencedjango
kimfalk/live-project
Mar 2020 - Dec 2021
Contributions:1 review, 10 PRs, 22 pushes in 1 year 9 months
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