Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches

While recommender systems are highly successful at helping users find relevant information online, they may also exhibit a certain undesired bias of mostly promoting only already popular items. Various approaches of quantifying and mitigating such biases were put forward in the literature. Most recently, calibration methods were proposed that aim to match the popularity of the recommended items with popularity preferences of individual users. In this paper, we show that while such methods are efficient in avoiding the recommendation of too popular items for some users, other techniques may be more effective in reducing the popularity bias on the platform level. Overall, our work highlights that in practice choices regarding metrics and algorithms have to be made with caution to ensure the desired effects.