## 6 Performance Evaluation and Results
### Comparative Study  
In the following section, a comparative study is conducted in order to demonstrate and evaluate the superior performance of the proposed RSVTM framework against other possible alternatives ranging from static targeted ones to completely random selections. Specifically, five comparative scenarios are examined with respect to the recommendation selection of visitors, which is described below. 1. RSVTM framework: each visitor acts as a stochastic learning automaton that learns through past actions and makes the most beneficial recommendation selection via the RSVTM algorithm,
2. Random Scenario: each visitor randomly selects a recommendation,
3. Fixed A (all visitors select Recommendation A),
4. Fixed B (all visitors select Recommendation B),
5. Fixed C (all visitors select Recommendation C). Aiming at providing indicative scalability results of the RSVTM framework while, at the same time, evaluating the performance of all the proposed approaches with varying visitor numbers. We consider increasing the number of visitors within the museum. We performed a detailed Monte Carlo analysis for the different sets of visitors by running 1000 runs of each comparative scenario. Figure 8a,b present visitors average QoE and total achieved QoE, respectively, as a function of the number of visitors in the museum. The results reveal that the fixed choice of the recommendation by visitors, i.e., Fixed A, Fixed B, and Fixed C scenarios, concludes to lower average and total QoE values when compared to the RSVTM framework due to the increased congestion at each recommendation when considered in isolation. The random choice of recommendation concludes an almost uniform distribution of visitors for the recommendations. Thus, the corresponding congestion phenomenon is limited or eliminated. Nevertheless, the random scenario achieves comparable results with respect to the QoE values with a fixed C scenario where, at the latter one, all the visitors selected the best available recommendation C in expectation of enjoying the highest possible satisfaction. In contrast to all the other four examined scenarios, the RSVTM framework demonstrates superior results regarding both the average and the total QoE values since it enables visitors to dynamically sense the congestion of each recommendation and make the most beneficial recommendation choice that will maximize their perceived QoE. In our framework, to maintain generality and flexibility, we did not a priori correlate visiting styles with recommendation types, but we let visitors select the recommendation type as the system evolves and based on the current conditions. Such an approach, besides its general applicability under different recommendation types and environments, typically leads to a more balanced distribution of the visitors among the recommendation types especially in cases of different visitor distributions and mix of visitor styles. This way, we avoid cases where all visitors would select a specific type of recommendation, which would negatively affect the museum operation. Furthermore, as expected, the average QoE decreases as the number of visitors increases for all the comparative  
Figure 7: (**a**) RSVTM Running time and number of visitors’ deviation (with respect to \(b=0.2\)) vs. learning rate \(b\). (**b**) Average QoE and average visiting time vs. learning rate \(b\). scenarios due to the increased museum congestion, which presents higher costs in order to enjoy a recommendation and forces visitors to invest higher visiting times for their museum tour. Lastly, Figure 9 presents the visiting time of the visitors as a function of the number of visitors in the museum. It is noted that average visiting time reported is constant (60 min) before a certain number of visitors (68 visitors in the graph), which corresponds to the minimum visiting value, as assumed in this experiment.