A survey on recommender systems for Internet of Things: techniques, applications and future directions

May Altulyan*, Lina Yao, Xianzhi Wang, Chaoran Huang, Salil S. Kanhere, Quan Z. Sheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2098-2132
Number of pages35
JournalComputer Journal
Volume65
Issue number8
DOIs
Publication statusPublished - Aug 2022

Keywords

  • deep learning
  • Internet of Things
  • machine learning
  • recommender system

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