Data privacy and fairness in recommender systems

In early 2020, IKEA made a promise to give back the 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 analyse performance of such systems under minimal data requirements. Second, we propose a Bayesian approach to recommendations that uses in-session active learning to give personalised content without collection of private data.