Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/96551
Citations | ||
Scopus | Web of ScienceĀ® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yao, L. | - |
dc.contributor.author | Sheng, Q. | - |
dc.contributor.author | Ngu, A. | - |
dc.contributor.author | Ashman, H. | - |
dc.contributor.author | Li, X. | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014, pp.855-858 | - |
dc.identifier.isbn | 9781450322577 | - |
dc.identifier.uri | http://hdl.handle.net/2440/96551 | - |
dc.description.abstract | With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web-based services, physical things are becoming an integral part of the emerging ubiquitous Web. In this paper, we focus on the things recommendation problem in Internet of Things (IoT). In particular, we propose a unified probabilistic based framework by fusing information across relationships between users (i.e., users'social network) and things (i.e., things correlations) to make more accurate recommendations. The proposed approach not only inherits the advantages of the matrix factorization, but also exploits the merits of social relationships and thing-thing correlations. We validate our approach based on an Internet of Things platform and the experimental results demonstrate its feasibility and effectiveness. | - |
dc.description.statementofresponsibility | Lina Yao, Quan Z. Sheng, Anne H.H. Ngu, Helen Ashman, Xue Li | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.rights | Copyright 2014 ACM | - |
dc.source.uri | http://dx.doi.org/10.1145/2600428.2609458 | - |
dc.subject | Internet of Things | - |
dc.subject | Recommendation | - |
dc.subject | Social networks | - |
dc.title | Exploring recommendations in Internet of Things | - |
dc.type | Conference paper | - |
dc.contributor.conference | 37th International ACM SIGIR Conference on Research & Development in Information Retrieval (6 Jul 2014 - 11 Jul 2014 : Gold Coast, Australia) | - |
dc.identifier.doi | 10.1145/2600428.2609458 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.