Recommender systems play a vital role in providing pertinent content across diverse domains, such as entertainment, social networks, healthcare, education, travel, cuisine, and tourism. This review offers a thorough examination of cutting-edge recommender systems, as well as hybrid recommender systems. Hybrid models, combining different recommendation approaches, have gained prominence in enhancing system performance. The study classifies several models of hybridization and arranges the literature depending on the hybrid model and the applied machine learning methods in each study. Additionally, a systematic literature review examines the landscape of recommender systems over the last few years, emphasizing the quantitative aspects of research in this field. The review explores challenges, data mining techniques, recommendation strategies. It identifies common issues, such as addressing cold-start, accuracy, scalability and data sparsity, and highlights emerging challenges, including adapting to evolving user contexts and tastes.. Given the ongoing significance of hybrid recommenders, the review proposes exploring fresh possibilities such as utilizing parallel hybrid algorithms, and handling more extensive datasets, to address the evolving requirements of users.

Artificial Intelligence, Collaborative Filtering, Recommendation System, Hybrid Recommendation System, Data Mining.

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