# Improving Recommendation Accuracy Using Social Network of Owners in Social Internet of Vehicles
## 5 Evaluation and Discussion
### Simulation Results and Discussion  
Simulation results and comparison of different strategies is given in Figure 4. These results are average values based on 100 simulation runs. In each simulation run, all the vehicles (attached to persons) have to perform 3 activities during a week. So, if we have 500 vehicles, the total activities within a week becomes 1500. The simulation was run for about 4030 iterations, where each iteration (or tick) equals one hour. Therefore, we have 24 weeks (each week having 168 hours) of simulation. The total activities, then, become equals 36,000 (by 1500 \(\times\) 24). The fractions of these 36,000 activities would result into Good, Bad, and No Visit parameters, against the selected strategy. To get answers for hypothesis 1 (stating that the recommendations using weak ties are more valuable than recommendations using strong ties in pure SloV), we compare ReplaceS (the case believing in strong ties) and ReplaceW (the case believing in weak ties) strategies. Figure 4a clearly shows that ReplaceW strategy resulted in a significantly high number of Good Visits than ReplaceS strategy. The number of Bad Visits, presented in Figure 4c, are also high in the case of ReplaceW strategy, against the ReplaceS strategy. However, ReplaceW strategy has almost none in No Visit category,  
Figure 4: Comparison of the replacement strategies (**b,d,f**) with and (**a,c,e**) without quality enforcement based on Good, Bad, and No Visits. whereas ReplaceS strategy resulted in almost all activities in the No Visit category, as illustrated in Figure 4e. This can be a significant outcome as the ReplaceW strategy manages to always provide an option, with probabilistically more novel information, which can be equally Good or Bad due to the dynamics of quality updates in the system, whereas the ReplaceS strategy just blocks all the POIs for all vehicles, which can be quite frustrating. A visual time series analysis also confirms it, when Figure 5a,b are compared with each other. This means that simulation results validated hypothesis 1 in its current form. To get answers of hypothesis 2 (which states that the recommendations using the most connected persons in the SN are less valuable than recommendations using a random person in the SN), we compare ReplaceM and ReplaceL strategies, which represent their corresponding effects described earlier. It is evidenced, from the results presented in Figure 4, that there is no difference between the number of Good Visits (Figure 4a), Bad Visits (Figure 4c), and No Visits (Figure 4e) between both the strategies. Hence, hypothesis 2 is invalidated. The comparison of Figure 6a,b, illustrating the visualized form of time series analysis, also confirms this. The implications of rejection of hypothesis 2 are interesting. The SN, having no relevance to the localization of nodes in the network, would not affect the quality of a localized phenomena, in the case that it has a dependence on information sharing. It does not matter if the information received is from a highly connected or a random friend. It is, however, suggested by the comparison of ReplaceS strategy with ReplaceM or ReplaceL that information sharing certainly helps the vehicles. Figure 5: Illustrating Good (colored green), Bad (colored yellow), and No Visits (colored red) without quality enforcement using time series for: (**a**) ReplaceS strategy and (**b**) ReplaceW strategy. To get answers for hypothesis 3 (which states that the quality enforcement mechanism would enhance user experience), we compare the results of strategies in column 1 with those in column 2 of Figure 4, correspondingly. For example, to compare Good Visits with and without the enforcement of quality, we consider Figure 4a--representing results for Good Visits without centralized enforcement--and Figure 4b--representing results for Good Visits with centralized quality control. It is clear from the results that quality enforcement increased the number of Good Visits, while it decreased Bad Visits and No Visits.