Unveiling the Latest Trends and Advancements in Machine Learning Algorithms for Recommender Systems: A Literature Review

Sara Shafiee*

*Corresponding author for this work

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

This paper presents a comprehensive literature review of the research and application of machine learning (ML) algorithms in recommender systems (RS). The study aims to identify recent trends, explore real-life applications, and guide researchers in positioning their research activities in this domain published in 2023 (Jan-June). The findings are categorized into different domains including education, healthcare, ML algorithms (auto-encoders and reinforcement learning), e-commerce, and digital journalism. The review highlights the enhanced recommendation accuracy, increased scalability, personalization and context awareness, diverse ML techniques, and strategies for handling cold start and data sparsity, and the foundation for future advancements in ML algorithms for RSs considering the application in manufacturing enterprises.
Original languageEnglish
JournalProcedia CIRP
Volume121
Pages (from-to)115-120
ISSN2212-8271
DOIs
Publication statusPublished - 2024
Event11th CIRP Global Web Conference - Perdue University, West Lafayette, United States
Duration: 24 Oct 202326 Oct 2023

Conference

Conference11th CIRP Global Web Conference
LocationPerdue University
Country/TerritoryUnited States
CityWest Lafayette
Period24/10/202326/10/2023

Keywords

  • Machine learning
  • Recommender system (RS)
  • Personalization
  • Review
  • Manufacturing
  • Scalability

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