CASARS Recommender Systems: A Systematic Review on Methods, Datasets, Limitations, and Future Directions

Abstract

Among the main objectives of recommender systems, the minimization of the problem known as information overload stands out. The personalization of recommendations, which emerge as users interact across various digital platforms, aims to mitigate this recurring issue in the modern world. Over the years, different recommendation paradigms have been explored, revealing both the potentialities and limitations of each technique and their respective proposed models. As a result, it has been shown that combining two recommendation paradigms, that is, the hybridization of methodologies, has the potential to be a more efficient alternative to meet the expectations and needs of these systems. Accordingly, this study presents a systematic literature review on recommender systems, focusing on two paradigms: context‐aware recommendation and session‐aware recommendation (CASARS). Our research seeks to elucidate which studies in the literature address recommendation strategies that leverage users' historical information to contribute to item recommendations throughout their ongoing sessions, as well as mechanisms for incorporating situational contextual information, that is, the user's context expressed strictly through interactions among items within the current session. Our goal is to outline the trajectory of the proposed models, identify the predominant application domains, critically analyze the evaluation methodologies, and, based on that, highlight the fundamental challenges and future research directions. In this study, we adopted a rigorous systematic review protocol, through which 25 articles were selected and thoroughly analyzed. The analysis reveals a clear chronological and conceptual progression of architectures, evolving from sequential models based on Recurrent Neural Networks to more complex structural representations with Graph Neural Networks, culminating in current approaches employing Transformers and attention mechanisms. We identified the main application focuses in domains such as e‐commerce, which accounts for 44% of the studies, and highlighted a critical gap in evaluation practices, characterized by the preference for accuracy metrics, such as Recall and NDCG, over dimensions essential to user experience, such as diversity, novelty, and serendipity. Although the field demonstrates rapid evolution and technical sophistication, future research directions were identified and thoroughly discussed.