## 7 Conclusions  
In this paper, the problem of optimizing the recommendation selection and the visiting time management in museums is studied under a QoE-driven game theoretic approach. Our work follows the principle that a museum is viewed as a Cyber Physical Social System where visitors act and make decisions in an environment that involves constraints. Each visitor evolves in a physical or virtual space with others where his/her behavior influences and is influenced by the others. Specifically, in our work, each museum visitor is considered a stochastic learning automaton who senses the museum environment (e.g., congestion, available recommendations, etc. ), gains knowledge  
Figure 8: QoE as a function of the number of visitors for all the comparative scenarios. (**a**) Average QoE. (**b**) Total QoE. Figure 9: Average visiting time as a function of the number of visitors for all the comparative scenarios. from his/her past actions, and makes the most beneficial recommendation selection by following the learning rules. Given the corresponding recommendation selections, each visitor aims at maximizing his/her perceived QoE in a distributed manner. The problem of visitor QoE maximization is formulated as a non-cooperative game among visitors and the existence and uniqueness of a Nash equilibrium is shown. A distributed iterative and low-complexity algorithm--RSVTM--is introduced and solves the joint recommendation selection and visiting time management. The RSVTM algorithm determines the recommendation selection for each visitor as well as the Nash equilibrium point of the visiting time management non-cooperative game. An extensive evaluation of the proposed RSVTM framework was conducted based on realistic parameters and data collected through a human in the loop experiment and a detailed questionnaire was circulated to museum visitors. The corresponding detailed numerical results demonstrated the operational effectiveness and efficiency of the proposed approach while comparative results in terms of achieved QoE confirmed the superiority of the RSVTM framework against other alternative recommendation approaches. Part of our current and future work focuses on studying the behavior of museum visitors in terms of recommendation selection and on improving their perceived QoE under the risk averse and risk seeking aspect of their decision-making process. Current models do not properly address the fact that individuals in real life do not necessarily behave as neutral expected utility maximizers, but they tend to exhibit such behavior especially under uncertainty. Therefore, integrating risk preferences in the involved utility function depicting such deviations in decision-making is of high research and practical importance. Moreover, under this perspective, the concept of announcing different pricing policies per recommendation will be examined as an incentive mechanism to deal with museum congestion. Furthermore, the routing of visitors within the museum as well as the overall planning of the visiting traffic and touring will be studied. Additionally, innovative intrinsic and extrinsic motivation mechanisms will be devised and paired with the previously mentioned incentive mechanism to improve the word-of-mouth reputation of a museum, increase the revisit and engagement of visitors, and facilitate the smooth operation of a museum while increasing its profits. Additionally, stochasticity in visitors' decision-making processes regarding their optimal visiting time can be considered, which introduces a stochastic time management game where its outcome may lead to variations in individuals' decisions. Lastly, the proposed model can be extended and adapted in Unmanned Aerial Vehicle (UAV)- assisted networks dealing with capturing mobile users' QoE levels from being served by the UAV or the macro base station [25; 26]. All authors contributed extensively to the work presented in this paper. [PERSON] had the original idea on which we have based our current work and had the overall coordination in the writing of the article.