A hybrid IoT services recommender system using social IoT

Nowadays, the increasing number of smart objects and devices which are connected to the Internet leads to the introduction of numerous IoT services. However, due to the overloading of information, many users suffer from several difficulties in obtaining useful and relevant services that meet their needs and interests. Recommender Systems (RSs) provide an efficient solution to this complex problem. Yet, the existing IoT recommender systems are based on objects owned by the users themselves because it is a serious problem, in IoT recommender systems, to find alternative sources of the missing data to fulfill the service requirements. Whereas, the IoT data generated by various objects is inefficiently used. An efficient IoT service recommendation can be reached based on Social IoT (SIoT) using the generated data by various IoT devices owned by friends and friends of friends. In this paper, we propose a hybrid technique that combines implicit collaborative filtering and ontology to recommend personalized IoT services to users. Ontology is used for modeling the SIoT, where we incorporate the social relationships among objects into the recommendation process alongside the ratings while collaborative filtering predicts ratings and generates recommendations. The evaluation results show that our proposed recommendation technique outperforms the collaborative filtering using SIoT on its own without SIoT in terms of personalization and recommendation accuracy.