## 6 Performance Evaluation and Results
### RSVTM Properties and Operation  
In this subsection, our main goal is to study and illustrate the key operational properties and characteristics of the RSVTM framework through a detailed simulation-based analysis. Towardsthis direction, we initially consider a basic scenario for demonstration purposes where \(N=10\) visitors are examined with the following distribution of visitors per visiting style, i.e., four ant visitors, two butterfly visitors, two fish visitors, and two grasshopper visitors. For the experiment execution, we assume the following parameter values since they have been extracted from the circulated questionnaire and mapped or normalized where needed to meaningful values for the performance analysis, \(Q_{A}=0.5\), \(Q_{B}=0.7\), \(Q_{C}=0.9\), \(c_{A}=3.2\cdot 10^{-3}\), \(c_{B}=5.8\cdot 10^{-3}\), \(c_{C}=10.2\cdot 10^{-3}\), \(b=0.3\), \(rt_{i}^{\text{target},b}=0.11\), \(rt_{i}^{\text{target},a}=0.12\), \(rt_{i}^{\text{target},f}=0.13\), \(rt_{i}^{\text{target},g}=0.14\) and \(t_{i}^{Min}=1\), \(t_{i}^{Max}=2\). The latter values refer to the visiting time constraints and are properly mapped to 60 min and 180 min, respectively. All simulations were performed on MATLAB in an Intel Core i5-7200U @2.50 GHz, 8.00 GBytes RAM laptop. Specifically, Figure 3a presents the achieved QoE levels of the visitors via the RSVTM algorithm as a function of their ID considering the recommendation that they have selected, i.e., A, B, C, and their visiting style, i.e., a, b, g, and f. The results reveal that the visitors even under the RSVTM approach tend to select the recommendation that can potentially provide higher QoE, i.e., recommendation C due to the fact that: \(Q_{A}<Q_{B}<Q_{C}\). However, as recommendation C becomes congested due to the visitors' multiple requests and a fact that would deteriorate as the number of visitors increases, it is observed that the visitors that selected this recommendation finally achieve lower QoE compared to the ones that selected recommendation B, e.g., visitors 4 vs. visitor 5 and visitor 6 vs. visitors 1 and 2. Based on this observation, we confirm that the most efficient choice in terms of recommendation selection is not always the one that can potentially provide the highest perceived QoE (i.e., recommendation C). This is explained by the fact that the potential increased congestion in the enhanced recommendation leads to the increased cost of waiting time and, thus, to lower perceived QoE, which is expressed in Equation (2). The latter outcome is also evident in Figure 3b where all visitors selected recommendation C, which provides greater QoE due to its advanced service. However, since recommendation C involves congestion, the average visitor achieved QoE is lower (horizontal line in Figure 3b) when compared to the RSVTM algorithm (Figure 3a). Please also note that some visitors even achieve negative QoE values in the case of fixed recommendation C since the cost associated with the choice of recommendation C, due to the congestion issue, becomes greater than the pure achieved satisfaction. Specifically, the grasshopper visitors (ID 7 and 8) achieve negative QoE given that they are less resilient when faced with the museum congestion, as discussed in Section 3.1 and presented in Figure 2. This problem is alleviated by the proposed RSVTM approach that manages a dynamic and better distribution of visitors to the various recommendation styles. Enabling visitors to sense their environment, learn from their past actions by behaving as stochastic learning automata, and make autonomous optimal decisions about themselves are the driving factors that allow the RSVTM algorithm to converge to higher QoE values for all visitors when compared to a priori choosing the best available recommendation by ignoring the congestion issue. Figure 3: Visitors’ QoE as a function of their ID (**a**) under the RSVTM algorithm and (**b**) assuming that all visitors select the best recommendation (Recommendation C) in terms of potentially achieved QoE.