Wolt's Discovery page serves as the primary gateway for millions of weekly users exploring diverse cuisines and products. With over 130,000 merchants in 25 countries, presenting relevant content poses a unique challenge. In this presentation, we address the complexities of personalizing the Discovery page using a hierarchical multi-armed bandit (MAB) approach built on the Python ecosystem. We outline the challenges specific to an expansive online delivery platform, introducing our MAB solution that incorporates hierarchical parameters at user, segment, city, and country levels. Leveraging Thompson Sampling for exploration and exploitation, our approach accommodates data sparsity challenges. Evaluation results, both offline and online, showcase the effectiveness of our solution. The talk concludes with insights into the resilient, scalable, and adaptive architecture underpinning our approach, featuring open-source libraries such as mlflow, Flyte, and Seldon Core. Our learnings and future steps toward a personalized, context-aware Discovery page cap off the presentation. Join us as we navigate the intricacies of recommendation challenges in the dynamic world of quick commerce.
Senior Data Scientist in Wolt's Personalization Team working on Carousel Ranking and Personalized Recommendations. Show Host of Recsperts - Recommender Systems Experts, the Podcast Show with industry and academia experts in Recommender Systems. Building Recommenders and Personalization Solutions with Python for various industries since 7+ years as well as creator and instructor of Python RecSys Training.
After graduating with a business information systems degree from HS Fresenius, Steffen started working as a Data Engineer at Fielmann (Germany's biggest optician), building their internal data platform. In 2021 he joined Capgemini as an IT consultant working in various roles and projects for a multitude of clients - mostly enterprises. In 2023 Steffen joined Wolt as a Machine Learning Engineer in the personalisation team as one of Wolt's first embedded MLEs. Steffen is also a co-organiser of the ML Ops Community Berlin.