Background. Recommendation systems have become indispensable components of modern digital platforms, enabling personalized content delivery across diverse domains. Traditional collaborative filtering and content-based approaches often fail to capture temporal dynamics and contextual dependencies inherent in user behavior patterns. Sequential recommendation systems (SRSs) and session-based recommendation systems (SBRSs) have emerged as new paradigms to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations. Objective. To propose a system approach for multicriteria evaluation of various SRS and SBRS models – a unified framework for understanding these models, selecting the best recommendation model, and guiding future research directions in temporal-aware recommendation systems. To provide a systematic overview and comprehensive analysis of session-based and sequential recommendation systems, to examine their theoretical foundations, evolution, empirical performance characteristics, and practical deployment considerations. Methods. A comprehensive analysis of foundational approaches from Markov chain models to modern neural architectures including attention-based methods, graph neural networks, and state-space models is conducted. The approaches are systematically categorized based on architectural principles, temporal modeling strategies, and knowledge integration methods. The Analytic Hierarchy Process is applied for calculation of relative importance of benefits, costs, opportunities and risks in a problem of session-based and sequential recommendation systems synthesis. An experimental study of various SRS and SBRS models was performed on benchmark datasets. Results. Empirical studies on temporal benchmark datasets show that combining SASRec and ReCODE improves the Recall@K metric by 9% over the baseline SASRec model, and combining GRU4Rec with ReCODE improves the metric by 17% over the baseline GRU4Rec. The SASRec model, which adapts transformer architectures to the sequential recommendation problem, achieved the highest baseline performance in terms of Recall@K and NDCG@K criteria on benchmark datasets compared to the other examined models, demonstrating the effectiveness of self-attention mechanisms for sequence modeling. ReCODE is a model-independent neural ordinary differential equation framework for recommender systems and an effective framework for studying consumer demand dynamics, has improved the metrics of existing baseline approaches, and has acceptable computational complexity for practical recommender system deployment scenarios. Conclusions. Session-based and sequential recommendation systems have evolved through several paradigmatic shifts with significant scientific achievements including establishment of session-based recommendation model as distinct from traditional collaborative filtering, development of attention mechanisms for sequence modeling, and introduction of continuous-time formulations. Future research directions include unified architectures, scalability solutions, improved evaluation methodologies, and extensions to multi-stakeholder scenarios.
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