CtrlBench-Rec: A Framework for Evaluating Controllability in Recommender Systems

Jul 16, 2026

Researchers introduce CtrlBench-Rec, a collaborative multi-agent framework designed to systematically assess the controllability of recommender systems, which are often treated as black boxes. The framework formalizes three key tasks—target content discovery, interest profile shaping, and popularity bias mitigation—to measure how well recommender systems can be steered by explicit or implicit user guidance. Experiments across real-world datasets and models show that CtrlBench-Rec quantifies controllability and highlights persistent challenges, such as resistance to promoting long-tail content.

Why it matters: CtrlBench-Rec provides the first standardized toolkit for evaluating and auditing the controllability of recommender systems, addressing a critical gap in algorithmic transparency and user empowerment.

Full story at: arXiv Information Retrieval ↗