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

By Ivens Da Silva Portugal, P. Alencar, D. Cowan. 2015

671 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lingxiao Huang
2. Kyungbok Min
3. Aminu Da’u
4. C. Arun
5. Naomie Binti Salim
6. Nisheeth K. Vishnoi
7. Hanxiang Wang
8. Cheol Hee Lee

In Information Systems

1. Lingxiao Huang
2. Aminu Da’u
3. Naomie Binti Salim
4. Nisheeth K. Vishnoi
5. Iqbal H. Sarker

Citations in the corpus

• Stable and Fair Classification (2019, background): “One example is recommendation systems that rely on classification algorithms (Park et al., 2012; Portugal et al., 2018).”
• Sensor-based and vision-based human activity recognition: A comprehensive survey (2020, background): “In addition, the use of shallow features leads to poor performance on incremental learning or unsupervised learning [40,41] .”
• Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study (2020, neutral): “Specific AI-technology reviews, but with no links to B&M subject e.g. [28].”
• Recommendation system based on deep learning methods: a systematic review and new directions (2019, methodology): “Alencar and Cowan (2018), used an SLR method to analyze the utilization of machine learning methods and their application domain for RS.”
• Rebooting employees: upskilling for artificial intelligence in multinational corporations (2021, background): “1 Further, AI itself comprises a set of algorithms which depends on data for executing the responses (Jaiswal & Arun, 2021; Portugal et al., 2018).”