Recommender systems (RS) are expanding rapidly as they help users navigate the vast amount of information available on the Web by suggesting the best choices. Numerous studies have been conducted to design high-precision recommendation models capable of predicting the right items for the right users. However, these models still face challenges such as cold start, scalability, and sparsity. To address these issues, we explore new context-aware recommendation techniques that leverage contextual parameters like user emotions, location, noise, and light level constraints to enhance recommendations and improve user satisfaction. This paper aims to address these challenges and the potential solutions. We discuss the life cycle of context-aware recommender systems, techniques for analyzing and determining users’ contextual preferences, and relevant contextual modeling approaches. By effectively addressing these issues, the implementation of context-aware recommender systems can be significantly improved.