Recommender Systems for the Internet of Things: A Survey
Information Retrieval
2020-07-15 v1 Machine Learning
Machine Learning
Abstract
Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dynamic, and heterogeneous IoT data. This paper presents a comprehensive review of the state-of-the-art recommender systems, as well as related techniques and application in the vibrant field of IoT. We discuss several limitations of applying recommendation systems to IoT and propose a reference framework for comparing existing studies to guide future research and practices.
Cite
@article{arxiv.2007.06758,
title = {Recommender Systems for the Internet of Things: A Survey},
author = {May Altulyan and Lina Yao and Xianzhi Wang and Chaoran Huang and Salil S Kanhere and Quan Z Sheng},
journal= {arXiv preprint arXiv:2007.06758},
year = {2020}
}