Exploring recommendations in Internet of Things

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.