Webinar "Learning through machine learning: how we built a recommendation system from scratch"

We at Metro.digital have been building data science products for years and have gathered a fair amount of experience, especially in the area of recommendation systems. Over the last few months, my team’s been focusing on an exciting new challenge: building from scratch a machine learning-based recommendation system for real-time identification of substitute products. The resulting benefits are multiple: customers quickly discover more from our assortment; Metro employees reduce their time spent on manual search; and, Metro itself increases revenue and retains customers. The aim of this talk is to tell the story of our journey from concept to production, with a focus on our learnings, mistakes, and results.

Speaker

Dora Petrella, Senior Data Scientist at METRO.digital. Dora is a curiosity-driven Data Scientist with 4 years of experience. Her background is in Engineering and Computer Science, and she is currently based in Germany. She chose to work for Metro.digital, the tech software company daughter of the international wholesaler Metro. Her Italian origins and her passion for everything around Machine Learning fit well in a company where food and technology meet together. Dora contributed to different Machine Learning projects in the wholesale sector, such as recommendation systems, ranking problems in the context of product search, personalization in the context of marketing, and customer segmentation. Her main tasks are researching solutions, contributing to the implementation and monitoring of machine learning-based algorithms, and presenting results even to non-technical audiences.