Recommender Systems for the Internet of Things: A Survey

July 14, 2020 ยท The Cartographer ยท ๐Ÿ› arXiv.org

๐Ÿ“š THE CARTOGRAPHER: The Cartographer
Survey/review paper โ€” maps the landscape rather than implementing a method.

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Authors May Altulyan, Lina Yao, Xianzhi Wang, Chaoran Huang, Salil S Kanhere, Quan Z Sheng arXiv ID 2007.06758 Category cs.IR: Information Retrieval Cross-listed cs.LG, stat.ML Citations 10 Venue arXiv.org Last Checked 3 days ago
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.
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