Abstract

In early 2020, IKEA promised to give back control to customers over their data. In this talk, we look at two aspects of the promise in the context of data-driven product recommendations. First, we analyze the performance of such systems under minimal data requirements. Second, we propose a Bayesian approach to recommendations that uses in-session active learning to give personalized content without collecting private data.

Martin Tegnér

Machine Learning Researcher @ IKEA Digital

Martin is a data science researcher at IKEA Group. He works with data-powered AI to enrich the customer experience at every point of the customer journey. In his current research, he develops algorithms that provide personalized content while respecting fair use of the customer’s data. Before joining IKEA, he was a researcher in the machine learning group at the University of Oxford.