The use of machine learning algorithms in recommender systems: A systematic review

• Published in: Expert systems with applications Vol. 97; pp. 205 - 227
• Main Authors: Portugal, Ivens, Alencar, Paulo, Cowan, Donald
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2018; Elsevier BV
• Subjects: Algorithms; Application domains; Artificial intelligence; Business metrics; Data management; Literature reviews; Machine learning; Machine learning algorithms; Performance measurement; Performance metrics; Recommender systems; Systematic review; Systematic review of the literature; Systematic review of the literature; Performance metrics; Machine learning algorithms; Recommender systems; Application domains; Machine learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2017.12.020

•A survey of machine learning (ML) algorithms in recommender systems (RSs) is provided.•The surveyed studies are classified in different RS categories.•The studies are classified based on the types of ML algorithms and application domains.•The studies are also analyzed according to main and alternative performance metrics.•LNCS and EWSA are the main sources of studies in this research field. Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics.