Recommender Systems for the Internet of Things: A Survey
This addresses the problem of adapting recommender systems to IoT data for researchers and practitioners, but it is incremental as it reviews existing work.
The paper surveys state-of-the-art recommender systems for the Internet of Things (IoT), highlighting that traditional systems fail to handle dynamic and heterogeneous IoT data, and proposes a reference framework to compare studies and guide future research.
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.