A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions. (19th May 2021)

• Record Type: Journal Article
• Authors: Altulyan, May; Yao, Lina; Wang, Xianzhi; Huang, Chaoran; Kanhere, Salil S; Sheng, Quan Z
• Is Part Of: Computer journal. Volume 65:Number 8(2022)
• Page Start: 2098
• Page End: 2132
• Publication Date: 2021-05-19
• Subjects: recommender system -- Internet of Things -- deep learning -- machine learning; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxab049
• Languages: English
• ISSNs: 0010-4620

Abstract

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.