# Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles
## 3 Combinatorial Framework  
In the proposed combinatorial framework, there are three types of entities (agents): a _vehicle_, a human or a _person_, and a _POI_. One vehicle is attached to one person, and it is used for visiting POIs. Each of the persons has a list of activities which it performs repeatedly as the simulation time progresses. An _activity_ consists of the POI to visit, time of visit, and duration to stay there. During a stay, the person experiences the Quality of Service (QoS) provided by the POI at that time. If the QoS is less than person's expectation, it is \"unhappy\"; otherwise, it is \"happy\". Although QoS also transforms as the simulation progresses, a person, once unhappy with a POI, would be unlikely to visit that POI again. However, a POI having low QoS may be replaced by another POI through vehicle and/or social level networking. Since the number of vehicles is much more than POIs, many vehicles gather at one POI at the same time. It enables them to create / update an SIoV that is essentially geographic in nature. The SIoV builds up with time and can be used to get a recommendation inferred from shared data about vehicles' previous experiences. One option can be to give preference (in the case of many recommendations) to more frequently encountered vehicles. Although this seems natural in social sense, it probably does not fit in the current scenario. When there are limited number of POIs to visit, two geographically co-located vehicles get more chances of activities, in which both target the same POI simultaneously. Thus, they may have the same experience. The same is validated by the theory of social networking, which suggests that weak ties have more chances of generating novel information compared with strong ties. The first hypothesis, given below, of this work is based on these two contradictory views:  
**Hypothesis 1**. : _Getting recommendation using weak ties rather than strong ties in SIoV would generate more novel information, thus helping in providing suitable recommendations._  
The work presented in Reference [38] used triadic closure trying to introduce novel information. Although this resulted in promising results in some cases, it compromised the nature of the network. Being geographic network, for a triadic closure, it is necessary that all vehicles involved in it are co-located. As a result, considering triadic closure between co-located vehicles practically merges the resulting network into the above hypothesis. Thus, we refrain to deal with it separately. To get potentially useful information, this work proposes the use of Social Network (SN) with the above network, in conjunction. Since the SN is constructed progressively using preferential attachments without geographic boundaries, it would represent the real world scenario. Even if the SN remain regular in nature, it may serve the purpose as its growth is not attached with geographic information and plans of other people. However, the point of contention is, its suitability to use recommendations from more connected or less connected friends. The main concern is how the connected components get novel information. SNs suggest getting it through bridges, which are essentially less connected nodes. This leads us towards the second hypothesis, given below:  
**Hypothesis 2**. : _Getting recommendation using most central nodes (having the highest degree) may not be a good option. A random friend would be a more suitable option in the current scenario._  
This work also intends to check the impact of quality enforcement (by some central authority) mechanism on achieving the quality of POIs and subsequently improving the overall user experience. Since successful recommendations are expected to improve user experiences, it produces quite a straightforward relationship between the quality enforcement agency and user experiences. This makes the third hypothesis of this work, stated below:  
**Hypothesis 3**.