# Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles
## 1 Introduction  
Inter-connectivity of all objects of the universe and, consequently, the full digitization of the fabric of life has been a scientific quest for quite long [1]. Many components of this vision are already in place, like the blocks of a puzzle. One of the most significant recent developments towards this, no doubt, is the Internet of Things (IoT) [2, 3]. Further, the last decade has seen exciting developments which are related to IoT. Following the \"Internet of-\" idiom, the Internet of Vehicles (IoV) [4] is already visible and ready to transform the way we live in modern cities. IoV is also known as Vehicular IoT (VIoT). The conquest of smart transportation for modern cities is not recent and probably started a few decades ago, resulting into technologies which are already deployed, such as Vehicular Adhoc NETworks (VANETs) [5] and Vehicular Social Network (VSN) [6, 7]. However, the conception of VIoT is well beyond the state-of-the-art and ready to leap into a chasm of possibilities, mainly towards social dimension of it--more recently termed as SloV [8]--whereby vehicles build and manage their own Social Networks (SNs). SloV has a potential of changing what we do and how we live in big cities. In particular, it can make our cities smarter and more sustainable. Since the intrinsic indulgence of SNs into our lives, the importance of context-aware recommendation services has become pervasive and apparent. Many times, these services are based onthe social dimensions of their users. VIoT is already being used to recommend useful and trustworthy services based on the profile and location of the vehicle [9; 10]. Similarly, vehicles' navigation and monitoring [11; 12; 13], managing network access [14], targeted advertising [15; 16], and drivers' behavior modeling [17] are some of the key examples of VIoT applications. Obviously, SIoV can provide an additional layer of social connectivity between vehicles that can be tapped to further enhance the quality of recommendations. From a humanistic viewpoint, all of us (or at least most of us) consider technologies to be subservient to humanity and expect technologies having an intrinsic attachment to us--rivers and passengers, in the case of SIoV. Therefore, when a vehicle is part of SIoV, it must build and deploy its network (of vehicles) in order to accomplish users' goals. But, then, the resolution of the dichotomy of vehicular and human social network turns out to be a real challenge. The problem is that the services at SN and SIoV are still quite restricted within their respective domains. Therefore, a systematic investigation about pros and cons of a combinatorial system is required, which is the focus of this paper. In this paper, we investigate the fusion of these two types of networks. We also quantify users' satisfaction with respect to their goal achievement, while making use of vehicular and social networks in combination. We investigate the competence of SIoV--with and without a social network--towards provisioning of accurate recommendations to the users in a dynamically changing environment. The dynamics of recommended resources and evolution of networks have an underpinning on basic assumptions about IoT connectivity, principles of social network evolution, and user profiling. The scenario taken up for this research is about three entities: humans, vehicles, and Point of Interests (POIs). Humans are expected to execute a plan of activities, where each activity is about visiting a POI. As a result of their visits, humans will experience quality of service provided by points of interest. If quality of service experienced by a human is not up to her expectations, she would not have any desire to execute that activity next time. Now, the question is how to replace such an activity by using a recommendation system continuously being evolved based on SIoV and SN in combination. It involves inclusion of many related aspects and models, as well as exploring all interesting situations.