Thijs Sluijter
Thijs Sluijter is a machine learning engineer in the Picnic Consumer ML team. He has a background in Artificial Intelligence at the University of Amsterdam with a focus on Information Retrieval and Recommender Systems. At Picnic his focus is on Recipe Recommendations and Recipe Search; at home his focus is on his two cats Apollo and Orpheus and ultra distance bike racing.
Session
Over the past year, we rebuilt our recipe recommender at Picnic. The project was prompted by a new recipe application UI, which changed our recommendation problem from retrieval to ranking. This forced us to rethink our recommendation pipeline by modularising it to allow personalisation in any context across our app, and rethinking our model choice from a fast, retrieval-optimised two-tower model to an expressive Deep & Cross Network (DCN) ranking model. Running these experiments at scale required rethinking the data pipeline, too. We rebuilt our training data pipeline using Polars and Apache Arrow to process tens of millions of interactions efficiently, construct purchase histories with lower memory overhead, and move features into PyTorch with minimal copying. Finally, we built a constrained Autoresearch loop in which coding agents proposed configurations, ran training, evaluated results, and recorded the next hypothesis, all while data splits, metrics, production constraints, and reproducibility controls remained fixed.