A Decentralized Recommendation Engine in the Social Internet of Things
Daniel Defiebre, Dimitris Sacharidis, Panagiotis Germanakos · 2020
In the Social Internet of Things (SIoT), the connected objects operate autonomously to request and provide information and services to end users. Following concepts and aspects from human social networks, the objects interact with each other, and over time develop trustworthy relationships. By mitigating security and privacy concerns, the benefit to end users is more effective and trustworthy services. In this work, we design a recommender system over SIoT. The recommender takes advantage of the social dynamics that drive the behavior and interactions of autonomous objects as they attempt to discover and return the best possible result. The main aim is to facilitate the optimum pairing of objects so as to enable effective recommendations.