2025-12-09 –, Auditorium
When we at Bol decided to personalize campaign banners, we did what many companies do: bought an expensive solution. As a software engineering team with zero data science experience, we integrated a third-party recommender system for €1 million annually, built the cloud infrastructure, and waited for results. After our first season, the data told a harsh truth—the third-party tool wasn't delivering value proportional to its cost. We faced a crossroads: accept mediocrity or build our own solution from scratch, tailored to our requirements and architecture.
We'll walk you through our journey of building a more intelligent and flexible recommendation system from the ground up, and how this journey saved us over a million euros per year. We will share the incremental steps that shaped our journey, alongside the valuable lessons learned along the way
We started our journey of developing an in-house personalized banner recommendation system at Bol, by replacing an expensive third-party tool. We are going to talk about our decisions from ML to explore-exploit balance of our recommendations, sharing our story of building the system from scratch.
Ali Kohan:
Data Scientist and AI consultant with a background in computer vision and deep learning. After several years working as a computer vision engineer, I am now focused on building recommendation and personalization systems. Passionate about applied AI, I enjoy exploring how intelligent systems can better understand and adapt to human behavior.
Reza Ebrahimpour:
Software engineer with a decade of experience building scalable, cloud-native systems in the JVM ecosystem. Passionate about microservice architectures, data-driven design, and high-availability services. Recently focused on integrating machine learning and AI solutions into production systems at Bol.