Context-aware recommender systems (CARS) adapt recommendations to the user's specific context, enhancing their relevance. This article explores how contextual information can be integrated into recommender systems, discussing various approaches and applications. Traditional systems use simple user models, often ignoring the "situated actions" concept. CARS, however, consider multiple contexts, such as time, location, and purpose, to improve recommendation accuracy. The concept of context is multifaceted, with different views (representational and interactional) influencing how it is modeled. The article classifies contexts into static and dynamic, fully observable and unobservable, leading to a 3x2 framework. It discusses how context can be incorporated into recommendation processes through prefiltering, postfiltering, and modeling. Applications include travel guides, information search, and music recommendation. Challenges include determining the right context, handling dynamic contexts, and integrating context into existing systems. Future research directions involve exploring alternative approaches beyond the representational view and refining classification methods for contextual information. The article highlights the importance of context in enhancing recommendation quality and the need for further research in this area.Context-aware recommender systems (CARS) adapt recommendations to the user's specific context, enhancing their relevance. This article explores how contextual information can be integrated into recommender systems, discussing various approaches and applications. Traditional systems use simple user models, often ignoring the "situated actions" concept. CARS, however, consider multiple contexts, such as time, location, and purpose, to improve recommendation accuracy. The concept of context is multifaceted, with different views (representational and interactional) influencing how it is modeled. The article classifies contexts into static and dynamic, fully observable and unobservable, leading to a 3x2 framework. It discusses how context can be incorporated into recommendation processes through prefiltering, postfiltering, and modeling. Applications include travel guides, information search, and music recommendation. Challenges include determining the right context, handling dynamic contexts, and integrating context into existing systems. Future research directions involve exploring alternative approaches beyond the representational view and refining classification methods for contextual information. The article highlights the importance of context in enhancing recommendation quality and the need for further research in this area.