A systematic review and research perspective on recommender systems

• Published in: Journal of big data Vol. 9; no. 1; pp. 1 - 36
• Main Authors: Roy, Deepjyoti, Dutta, Mala
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 03.05.2022; Springer Nature B.V; SpringerOpen
• Subjects: Big Data; Collaborative filtering; Communications Engineering; Computational Science and Engineering; Computer Science; Content-based filtering; Data Mining and Knowledge Discovery; Database Management; Deep learning; Information Storage and Retrieval; Machine learning; Mathematical Applications in Computer Science; Networks; Performance measurement; Recommender system; Recommender systems; Review; Survey Paper; Systematic review; Taxonomy; Deep learning; Review; Collaborative filtering; Content-based filtering; Recommender system; Machine learning
• ISSN: 2196-1115; 2196-1115
• DOI: 10.1186/s40537-022-00592-5

Recommender systems are efficient tools for filtering online information, which is widespread owing to the changing habits of computer users, personalization trends, and emerging access to the internet. Even though the recent recommender systems are eminent in giving precise recommendations, they suffer from various limitations and challenges like scalability, cold-start, sparsity, etc. Due to the existence of various techniques, the selection of techniques becomes a complex work while building application-focused recommender systems. In addition, each technique comes with its own set of features, advantages and disadvantages which raises even more questions, which should be addressed. This paper aims to undergo a systematic review on various recent contributions in the domain of recommender systems, focusing on diverse applications like books, movies, products, etc. Initially, the various applications of each recommender system are analysed. Then, the algorithmic analysis on various recommender systems is performed and a taxonomy is framed that accounts for various components required for developing an effective recommender system. In addition, the datasets gathered, simulation platform, and performance metrics focused on each contribution are evaluated and noted. Finally, this review provides a much-needed overview of the current state of research in this field and points out the existing gaps and challenges to help posterity in developing an efficient recommender system.