Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/100404
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Type: Journal article
Title: Things of interest recommendation by leveraging heterogeneous relations in the internet of things
Author: Yao, L.
Sheng, Q.Z.
Ngu, A.H.H.
Li, X.
Citation: ACM Transactions on Internet Technology, 2016; 16(2):9-1-9-25
Publisher: Association for Computing Machinery
Issue Date: 2016
ISSN: 1533-5399
1557-6051
Statement of
Responsibility: 
Lina Yao, Quan Z. Sheng, Anne H. H. Ngu, Xue Li
Abstract: 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.
Keywords: Internet of things; data mining; hypergraph; latent relationships; recommendation
Rights: Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies show this notice on the first page or initial screen of a display along with the full citation. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, to redistribute to lists, or to use any component of this work in other works requires prior specific permission and/or a fee. Permissions may be requested from Publications Dept., ACM, Inc., 2 Penn Plaza, Suite 701, New York, NY 10121-0701 USA, fax + 1 (212) 869-0481, or permissions@acm.org. © 2016 ACM
RMID: 0030047464
DOI: 10.1145/2837024
Grant ID: http://purl.org/au-research/grants/arc/FT140101247
http://purl.org/au-research/grants/arc/DP140100104
http://purl.org/au-research/grants/arc/DE160100509
Appears in Collections:Computer Science publications

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