  This survey paper provides a comprehensive analysis of big data algorithms in
recommendation systems, addressing the lack of depth and precision in existing
literature. It proposes a two-pronged approach: a thorough analysis of current
algorithms and a novel, hierarchical taxonomy for precise categorization. The
taxonomy is based on a tri-level hierarchy, starting with the methodology
category and narrowing down to specific techniques. Such a framework allows for
a structured and comprehensive classification of algorithms, assisting
researchers in understanding the interrelationships among diverse algorithms
and techniques. Covering a wide range of algorithms, this taxonomy first
categorizes algorithms into four main analysis types: User and Item
Similarity-Based Methods, Hybrid and Combined Approaches, Deep Learning and
Algorithmic Methods, and Mathematical Modeling Methods, with further
subdivisions into sub-categories and techniques. The paper incorporates both
empirical and experimental evaluations to differentiate between the techniques.
The empirical evaluation ranks the techniques based on four criteria. The
experimental assessments rank the algorithms that belong to the same category,
sub-category, technique, and sub-technique. Also, the paper illuminates the
future prospects of big data techniques in recommendation systems, underscoring
potential advancements and opportunities for further research in this field
