### Paper Contributions and Outline  
The basic contributions of our proposed approach and framework are summarized below. 1. For the first time in the literature, visitors are modeled as learning automata via a reinforcement learning mechanism, which allows them to select the most appropriate recommendation for their museum tour. The concept of learning automata is adopted from the control theory where the learning automata entities can make autonomous decisions and selections among available choices by sensing their environment and based on their past decisions. Thus, visitors are able to intelligently sense their environment (e.g., actions of other visitors) while keeping track of their own decisions in order to take more educated and advantageous actions in the future. As time evolves, their decisions converge and visitors select the type of recommendation that will improve their perceived QoE. 2. We introduce a holistic approach of QoE-based visiting time management in museums. Each museum visitor aims to maximize his/her perceived QoE in a distributed manner for the selected recommendation. Based on the non-cooperative and distributed nature of managing visiting time, we formulate a maximization problem for each visitor's QoE function and confront it as a non-cooperative game. To deal with this problem while considering the different types of visitor personas, we follow an approach based on the quasi-concavity of the visitor QoE function in order to conclude at a unique Nash equilibrium point. 3. The perceived satisfaction of museum visitors is reflected and represented in a formal and quantitative way by using appropriately defined QoE functions. They are based on the following attributes: (a) time spent on a museum visit, (b) selected type of recommendation, and (c) fulfillment of visitor QoE prerequisites. The key novelty in this work is that the QoE function consists of the following two parts: (a) the pure QoE function and (b) the congestion control function. The pure QoE function reflects the tradeoff of achievable QoE over the time spent on the visit. The congestion control function is sophisticatedly selected in order to achieve fairness among museum visitors with respect to accessing and spending time in the museum. 4. The proposed framework enables autonomic visitor-centric management and QoE optimization in a personalized manner, which allows self-* properties, e.g., self-optimization, self-adaptation, etc. A two-stage distributed algorithm is proposed to determine the optimal visiting time and recommendation selection for each museum visitor. The output of the visiting time management problem feeds the learning system in a recursive manner in order to build knowledge. 6. Detailed numerical results are provided that demonstrate the performance and operational effectiveness and efficiency of the proposed algorithm along with its flexibility and adaptability under various scenarios. The proposed framework was tested with data provided by users via a questionnaire and additional simulation results were then generated to achieve a more thorough analysis. Lastly, the performance of the proposed framework is evaluated against the related state-of-the-art framework and its superiority in terms of achieved QoE is demonstrated. The rest of the paper is organized as follows. In Section 2, a high-level description of the joint recommendation selection and visiting time management framework and process is presented, which identifies all the involved elements and their respective roles and relations. In Section 3, some preliminary work as well as the introduced models and assumptions that are used throughout this paper are provided. Section 4 contains all the details of the recommendation selection mechanism. The formulation and solution of the reinforcement-learning framework are described via a game theoretic approach of the corresponding time management problem by optimizing visitor QoE.