Researchers propose a new task in recommender systems called pairwise interpretation of item rankings. This involves answering the question 'Why is item A ranked higher than item B?' They argue that effective solutions to this task must be grounded in the operation of the recommendation algorithm itself. To address this, they introduce a class of techniques based on counterfactual learning, which can identify items contributing to relative rankings. The authors demonstrate the feasibility of their approach using multiple datasets. --- Why it matters: This work matters because it tackles a long-standing challenge in recommender systems: providing users with meaningful explanations for why certain items are ranked higher than others. By enabling pairwise interpretation of item rankings, this research has implications for improving user trust and satisfaction with recommendation algorithms. Source: https://arxiv.org/abs/2608.21662