Fuzzy Rough Set based Social IoT Recommender System

Internet of Things (IoT) has brought a revolution in the lives of citizens by providing safety and comfort. Users are more concerned with the Quality of Experience (QoE) while using an application. One such application that enriches the users' experience is the assistance in the form of a recommender system. Recommending IoT services which are beneficial to the users based on their preferences is challenging task keeping in view of the diversity of the information required. The existing solutions incorporate the limited knowledge of the user and the context. However, to the best of the authors' knowledge, there is no efficient solution that recommends the users while taking into account the overall social IoT system. In order to address this problem, the recommender proposed in this paper uses a fuzzy rough set theory which is based on a strong mathematical foundation. The solution proposes an algorithm based upon the collaborative filtering which enables the user to discover new content dissimilar to the items viewed by them in the past. The simulation results prove that superiority of the solution against the standard parameters like Mean Reciprocal Rank (MRR), Mean Average Precision (MAP), Normalized Discounted Cumulative Gain (NDCG) and the state-of-art solutions.