# The Use of Machine Learning Algorithms in Recommender Systems: A Systematic Review

arXiv: https://arxiv.org/abs/1511.05263 · HTML: https://mlsanity.com/paper/1511.05263/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2015.

## Built on by (most cited first, 27 of 27)

- [On permutation-invariant neural networks](https://mlsanity.com/paper/2403.17410/) (2024)
- [VISTA: Vision-Language Inference for Training-Free Stock Time-Series Analysis](https://mlsanity.com/paper/2505.18570/) (2025)
- [Centroid Distance Distillation for Effective Rehearsal in Continual Learning](https://mlsanity.com/paper/2303.02954/) (2023)
- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- [Zero-Shot Recommendations with Pre-Trained Large Language Models for Multimodal Nudging](https://mlsanity.com/paper/2309.01026/) (2023)
- [Efficient Classification with Counterfactual Reasoning and Active Learning](https://mlsanity.com/paper/2207.12086/) (2022)
- [A Recommendation Approach based on Similarity-Popularity Models of Complex Networks](https://mlsanity.com/paper/2210.07816/) (2022)
- [A Hinge-Loss based Codebook Transfer for Cross-Domain Recommendation with Nonoverlapping Data](https://mlsanity.com/paper/2108.01473/) (2021)
- [Emergent Instabilities in Algorithmic Feedback Loops](https://mlsanity.com/paper/2201.07203/) (2022)
- [Maximum entropy exploration in contextual bandits with neural networks and energy based models](https://mlsanity.com/paper/2210.06302/) (2022)
- [A Human-ML Collaboration Framework for Improving Video Content Reviews](https://mlsanity.com/paper/2210.09500/) (2022)
- [Simulated Contextual Bandits for Personalization Tasks from Recommendation Datasets](https://mlsanity.com/paper/2210.10631/) (2022)
- [Assessing AI Utility: The Random Guesser Test for Sequential Decision-Making Systems](https://mlsanity.com/paper/2407.20276/) (2024)
- [Deep Reinforcement Learning-Based Product Recommender for Online Advertising](https://mlsanity.com/paper/2102.00333/) (2021)
- [Unavailable Transit Feed Specification: Making it Available with Recurrent Neural Networks](https://mlsanity.com/paper/2102.10323/) (2021)
- [A Novel Paper Recommendation Method Empowered by Knowledge Graph: for Research Beginners](https://mlsanity.com/paper/2103.08819/) (2021)
- [A Systematic Literature Review on Process-Aware Recommender Systems](https://mlsanity.com/paper/2103.16654/) (2021)
- [Predicting Solar Flares with Remote Sensing and Machine Learning](https://mlsanity.com/paper/2110.07658/) (2021)
- [Combinations of Jaccard with Numerical Measures for Collaborative Filtering Enhancement: Current Work and Future Proposal](https://mlsanity.com/paper/2111.12202/) (2021)
- [Applications of Machine Learning in Healthcare and Internet of Things (IOT): A Comprehensive Review](https://mlsanity.com/paper/2202.02868/) (2022)
- [Machine Learning based Anomaly Detection for Smart Shirt: A Systematic Review](https://mlsanity.com/paper/2203.03300/) (2022)
- [A Brief Overview of AI Governance for Responsible Machine Learning Systems](https://mlsanity.com/paper/2211.13130/) (2022)
- [Share, Collaborate, Benchmark: Advancing Travel Demand Research through rigorous open-source collaboration](https://mlsanity.com/paper/2306.06194/) (2023)
- [CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy Protection](https://mlsanity.com/paper/2308.11066/) (2023)
- [Robust Calibration For Improved Weather Prediction Under Distributional Shift](https://mlsanity.com/paper/2401.04144/) (2024)
- [Assistive AI for Augmenting Human Decision-making](https://mlsanity.com/paper/2410.14353/) (2024)
- [BanditWare: A Contextual Bandit-based Framework for Hardware Prediction](https://mlsanity.com/paper/2506.13730/) (2025)

## Abstract (arXiv)

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.