A comprehensive survey of recommendation methods and techniques

Journal of Big Data

卷:13

Survey

Angelica Johanna Ellice Andrews¹  Borko Furht¹ 

[1] Department of Computer Science, Florida Atlantic University, Boca Raton, Florida, United States of America

关键词: Recommendation systems;    Recommendation techniques;    Generational recommendation systems;    Recommendation system analysis;    Collaborative filtering;    Hybrid recommendation models;    Content-based filtering;   

DOI  :  10.1186/s40537-026-01493-7

 received in 2025-05-18, accepted in 2026-06-03,  发布年份 2026

来源: Springer

【 摘 要 】

With a substantial increase in digital information on various platform browsing applications, recommendation systems are imperative to filter large amounts of information in more palatable ways through the organization of the most relevant topics. This can be applied to various domains, including e-commerce, education, books, movies, music, and more. As such, it is crucial to have a thorough understanding of the various generations of recommendation systems. In this study, we conduct a comprehensive examination of generations of recommendation systems using various applicable techniques. To examine the objective of recommendation systems, we introduce challenges to recommendation systems. The generations are then broken down into sub-classes for further examination. These are content-based, collaborative filtering, hybrid models, matrix factorization, web usage mining, personality-based models, collaborative filtering using deep learning techniques, deep content-based models, and combined modeling of users and items using reviews. First-generation techniques often employ rudimentary approaches, whereas second- and third-generation techniques utilize more complex models that delve deeper into the vast amount of available data. Based on this analysis, we believe that our survey enhances the understanding of generational recommendation system models and highlights the importance of selecting suitable techniques to design novel, cross-disciplinary models.

【 授权许可】

CC BY-NC-ND   
© The Author(s) 2026

【 图 表 】

Figure 1. Generation of adapted anoikis-resistant cells.

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Figure 2. Histology and immunostaining of organoids.

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