Situation-aware recommender systems: a systematic review and framework for trustworthy recommendations

Over the past decade, recommender systems have become pivotal across digital platforms, supporting tasks such as media choice, e-commerce navigation, industrial decision support, and personalized learning. By analyzing user behaviors and preferences, modern engines enable filtering, ranking, and adaptive interaction at scale. A recent research trend concerns situation-aware recommenders, that are systems able to perceive and interpret surrounding conditions to adapt their output and anticipate user goals. These systems are increasingly shaped by the need for transparency, reliability, and alignment with trustworthy AI principles. Despite growing interest, the literature lacks a clear conceptual definition of "situation", a distinction from "context", and unified models, design guidelines, and evaluation frameworks for truly situation-aware recommenders. Consequently, only a limited subset of deployed solutions integrates situation awareness in an explicit and systematic way. This work presents a systematic review and classification of modern situation-aware recommender systems, highlighting the most used techniques, domains of application, open issues, and research challenges. The review follows the PRISMA methodology for systematic literature studies. The analysis is completed with the proposal of a reference framework grounded in Endsley's three-level Situation Awareness model and aligned with emerging principles of trustworthy AI. The architecture is used to identify key challenges and outline research directions in the field. It also serves as a comparative lens for existing work and as a blueprint intended to guide the development of the next generation of transparent, reliable, and human-centered recommender systems.