Understanding grey sheep users in recommender systems: A systematic and critical review of detection, modeling, and evaluation

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Grey sheep users (GSUs), characterized by weak or unstable alignment with the broader user population, remain a challenge in collaborative filtering-based recommender systems. Despite extensive efforts devoted to their detection and treatment, a clear and consistent understanding of GSUs is still lacking. Existing studies typically adopt model-dependent definitions and often assume that GSUs necessarily reduce recommendation quality. This paper presents a systematic and critical review of GSUs, covering their definition, detection strategies, modeling paradigms, and evaluation practices. We revisit the classical similarity-based interpretation and show that the effect of GSUs depends on model design rather than being inherently harmful. Based on this observation, we introduce a refined conceptual perspective that describes GSUs as users with consistently misaligned preference patterns, and further propose a dynamic view that distinguishes between short-term irregular behavior and stable preference differences. We then provide a comprehensive taxonomy of existing approaches, including detection-dependent methods and detection-free strategies, and analyze their underlying assumptions and limitations. We further show that the form in which a detection result is expressed constrains which handling strategies are available, and that this correspondence is rarely exploited in practice. A key finding of this study is that current research is limited by weak evaluation practices. In particular, the absence of ground truth, inconsistent definitions, and dependence on aggregate performance metrics lead to circular reasoning and unreliable conclusions. Finally, we outline future research directions toward more robust modeling frameworks and user-centered evaluation protocols, with the aim of establishing a clearer conceptual foundation and guiding the development of more reliable and inclusive recommender systems.