A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Abstract Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, tr...

Full description

Saved in:
Bibliographic Details
Published inComputer journal Vol. 65; no. 8; pp. 2098 - 2132
Main Authors Altulyan, May, Yao, Lina, Wang, Xianzhi, Huang, Chaoran, Kanhere, Salil S, Sheng, Quan Z
Format Journal Article
LanguageEnglish
Published Oxford University Press 11.08.2022
Subjects
deep learning
recommender system
Internet of Things
machine learning
Online AccessGet full text

Cover

More Information
Summary:Abstract Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.
ISSN:0010-4620
1460-2067
DOI:10.1093/comjnl/bxab049