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 language | English |
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Pages (from-to) | 2098-2132 |
Number of pages | 35 |
Journal | Computer Journal |
Volume | 65 |
Issue number | 8 |
DOIs | |
Publication status | Published - Aug 2022 |
Keywords
- deep learning
- Internet of Things
- machine learning
- recommender system