Things of interest recommendation by leveraging heterogeneous relations in the internet of things

Yao, Lina, Sheng, Quan Z., Ngu, Anne H. H. and Li, Xue (2016) Things of interest recommendation by leveraging heterogeneous relations in the internet of things. ACM Transactions on Internet Technology, 16 2: . doi:10.1145/2837024


Author Yao, Lina
Sheng, Quan Z.
Ngu, Anne H. H.
Li, Xue
Title Things of interest recommendation by leveraging heterogeneous relations in the internet of things
Journal name ACM Transactions on Internet Technology   Check publisher's open access policy
ISSN 1557-6051
1533-5399
Publication date 2016-04-01
Year available 2016
Sub-type Article (original research)
DOI 10.1145/2837024
Open Access Status Not Open Access
Volume 16
Issue 2
Total pages 25
Place of publication New York, NY, United States
Publisher A C M Special Interest Group
Collection year 2017
Language eng
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.
Keyword Data mining
Hypergraph
Internet of things
Latent relationships
Recommendation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
 
Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in Thomson Reuters Web of Science Article
Scopus Citation Count Cited 1 times in Scopus Article | Citations
Google Scholar Search Google Scholar
Created: Tue, 10 May 2016, 02:04:15 EST by System User on behalf of Learning and Research Services (UQ Library)