Exploring recommendations in Internet of Things

Lina Yao, Quan Z. Sheng, Anne H.H. Ngu, Helen Ashman, Xue Li

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

30 Citations (Scopus)

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 thingthing correlations. We validate our approach based on an Internet of Things platform and the experimental results demonstrate its feasibility and effectiveness.

Original languageEnglish
Title of host publicationSIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval
Place of PublicationNew York
PublisherAssociation for Computing Machinery
Pages855-858
Number of pages4
ISBN (Print)9781450322591
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014 - Gold Coast, QLD, Australia
Duration: 6 Jul 201411 Jul 2014

Other

Other37th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2014
CountryAustralia
CityGold Coast, QLD
Period6/07/1411/07/14

Keywords

  • Internet of things
  • Recommendation
  • Social networks

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