# Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles
## 2 Related Work  
[PERSON] et al. presented one of the earliest social networking frameworks, with the name RoadSpeak, for VSNs, where the commuters can form interest groups (social communities) based on their spatial and temporal preferences and can chat with each other through a messaging application [20]. A crowed-sourcing-based VSN, with the name SocialDrive, was presented by [PERSON] et al. to help in reducing the fuel consumption and improving driver's safety [21]. Another traditional VSN-based SN was presented in Reference [22], where the drivers can share their driving experiences with other drivers, and these experiences are aggregated based on the spatial and temporal dimensions. Roadcast is another VSN, where the vehicles can query other vehicles, and the most relevant information is retrieved based on the keywords in the query [23]. In Reference [24; 25], the authors used agent-based methodology for modeling and evaluating VSNs and presented detailed case studies of VSN applications in the context of smart cities. [PERSON] et al. presented a semantic-based multi-agent framework for VSN applications that utilizes the software agents and semantic schemes [26]. Their work was further extended in Reference [27] by providing a service-oriented platform that improves the transportation efficiency through collaboration among the commuters. Context-ware mechanism was utilized for predicting traffic congestion. [PERSON] et al. proposed the idea of SIoT, where the IoT devices form groups based on common goals [28]. SIoV was introduced by [PERSON] et al., where vehicles were modeled as social objects as an application of SIoT [29]. A middleware was introduced as an extension of intelligent transportation system for SIoT [30]. [PERSON] et al. proposed an architecture for SIoV, where various components, their interaction, and modalities of interaction were discussed in detail [31]. Similarly, the behavior of vehicles as social objects, their interaction and relationships were explored in detail in TNote [32]. S-Aframe [33] is a multi-layer framework with context-aware semantic service that utilizes the context information for providing a framework for developing SIoV applications. S-Aframe is an agent-based multi-layer framework with context-aware semantic service that uses the context information and provides a platform for the development of applications for SIoV [33]. Quite a few articles have proposed frameworks, techniques, and algorithms about SIoV applications. Obviously, the most intuitive set of applications are related to traffic itself. For example, authors in Reference [34] proposed an algorithm that could help analyze the reasons of traffic congestion, based on complex network of vehicles. But, the mechanism only uses a vehicular social network. Another recent publication [35] similarly enhances the urban mobility as a whole using a vehicular social network. In this work, authors also used social network analysis and social network concepts--termed as SNA and SNC, respectively--to propose solutions for urban mobility, but again, they do not take a human social network as a separate entity. Conceptually, the closest research contribution is from [PERSON] et al. [36]. In this work, a combinatorial framework is combined into SIoV having human social network as a component. They provide two definitions corresponding to two different behaviors that may coexist in such a unified SIoV architecture: a Vehicle-Oriented SIoV (VO-SIoV) and a Driver-Oriented SIoV (DO-SIoV). They performed a general comparison between these two behaviors, but they did not apply it on a combined application. Consequently, an application specific interface between two networks is not defined. In this paper, we studied information dissemination in terms of a recommendation system, which is entangled to the users' goals and quality of experience that leads to successful SIoV strategies.