The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review
This is an incremental review that helps researchers and practitioners understand algorithm usage and focus research efforts in recommender system development.
This paper conducted a systematic review to analyze the use of machine learning algorithms in recommender systems, finding that Bayesian and decision tree algorithms are widely used due to their simplicity, and identified research opportunities in requirement and design phases for software engineering.
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 a recommender system using a machine learning algorithm often has problems and open questions that must be evaluated, so software engineers know where to focus research efforts. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies research opportunities for software engineering research. The study concludes that Bayesian and decision tree algorithms are widely used in recommender systems because of their relative simplicity, and that requirement and design phases of recommender system development appear to offer opportunities for further research.