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Things of Interest Recommendation by Leveraging Heterogeneous Relations in the Internet of Things by Lina Yao; Quan Z. Sheng; Anne H. H. Ngu; Xue Li is a Computer Science article available to read on EtoBox.

What is Things of Interest Recommendation by Leveraging Heterogeneous Relations in the Internet of Things about?

The emerging Internet of Things (IoT) bridges the gap between the physical and the digital worlds, which enables a deeper understanding of user preferences and behaviors. The rich interactions and relations between users and things call for effective and efficient recommendation approaches to better meet users’ interests and needs. In this article, we focus on the problem of things recommendation in IoT, which is important for many applications such as e-Commerce and health care. We discuss the new properties of recommending things of interest in IoT, and propose a unified probabilistic factor based framework by fusing relations across heterogeneous entities of IoT, for example, user-thing relations, user-user relations, and thing-thing relations, to make more accurate recommendations. Specifically, we develop a hypergraph to model things’ spatiotemporal correlations, on top of which implicit things correlations can be generated. We have built an IoT testbed to validate our approach and the experimental results demonstrate its feasibility and effectiveness.

Who reads Things of Interest Recommendation by Leveraging Heterogeneous Relations in the Internet of Things?

Author

Lina Yao; Quan Z. Sheng; Anne H. H. Ngu; Xue Li

Publisher

ACM

Published

2016

Language

EN

Field

Computer Science (Physical Sciences)