Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/100404
Citations
Scopus Web of Science® Altmetric
?
?
Full metadata record
DC FieldValueLanguage
dc.contributor.authorYao, L.-
dc.contributor.authorSheng, Q.-
dc.contributor.authorNgu, A.-
dc.contributor.authorLi, X.-
dc.date.issued2016-
dc.identifier.citationACM Transactions on Internet Technology, 2016; 16(2):9-1-9-25-
dc.identifier.issn1533-5399-
dc.identifier.issn1557-6051-
dc.identifier.urihttp://hdl.handle.net/2440/100404-
dc.description.abstractThe 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.-
dc.description.statementofresponsibilityLina Yao, Quan Z. Sheng, Anne H. H. Ngu, Xue Li-
dc.language.isoen-
dc.publisherAssociation for Computing Machinery-
dc.rightsPermission 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-
dc.subjectInternet of things; data mining; hypergraph; latent relationships; recommendation-
dc.titleThings of interest recommendation by leveraging heterogeneous relations in the internet of things-
dc.typeJournal article-
dc.identifier.doi10.1145/2837024-
dc.relation.granthttp://purl.org/au-research/grants/arc/FT140101247-
dc.relation.granthttp://purl.org/au-research/grants/arc/DP140100104-
dc.relation.granthttp://purl.org/au-research/grants/arc/DE160100509-
pubs.publication-statusPublished-
Appears in Collections:Aurora harvest 7
Computer Science publications

Files in This Item:
File Description SizeFormat 
RA_hdl_100404.pdf
  Restricted Access
Restricted Access692.67 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.