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

翻译：本综述论文对推荐系统中的大数据算法进行了全面分析，旨在弥补现有文献在深度与精确性方面的不足。本文提出了一种双管齐下的方法：对现有算法进行深入分析，并提出一种新颖的分层分类体系以实现精确归类。该分类体系基于三级层次结构，从方法论类别开始，逐步细化至具体技术。这一框架能够对算法进行结构化、系统化的分类，帮助研究者理解不同算法与技术之间的相互关系。该分类体系涵盖广泛算法，首先将算法划分为四大分析类型：基于用户与项目相似性的方法、混合与组合方法、深度学习与算法方法、以及数学建模方法，并进一步细分为子类别与技术。本文融合了经验评估与实验评估以区分各项技术。经验评估基于四项标准对技术进行排序；实验评估则对属于同一类别、子类别、技术及子技术的算法进行排名。此外，本文还阐明了大数据技术在推荐系统中的未来前景，强调了该领域潜在的研究进展与机遇。