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

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

Research output: Contribution to journalArticlepeer-review

50 Citations (Scopus)

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.

Original languageEnglish
Article number9
Pages (from-to)1-25
Number of pages25
JournalACM Transactions on Internet Technology
Volume16
Issue number2
DOIs
Publication statusPublished - Apr 2016
Externally publishedYes

Keywords

  • Data mining
  • Hypergraph
  • Internet of things
  • Latent relationships
  • Recommendation

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