Journal
COMPUTER JOURNAL
Volume 65, Issue 8, Pages 2098-2132Publisher
OXFORD UNIV PRESS
DOI: 10.1093/comjnl/bxab049
Keywords
recommender system; Internet of Things; deep learning; machine learning
Ask authors/readers for more resources
This article provides a comprehensive review of recommender systems for the Internet of Things (RSIoT), including the related techniques, applications, and limitations. It also proposes a reference framework to guide future research and practices.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available