To provide fully immersive mobile experiences, next-generation touristic services will rely on the high bandwidth and low latency provided by the 5G networks and the Multi-access Edge Computing (MEC) paradigm. Recommendation algorithms, being integral part of travel planning systems, devise personalized tour itineraries for a user considering the popularity of the Points of Interest (POIs) of a city as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., augmented reality) the tourist will consume in the POIs and the quality in which such applications will be delivered by the MEC infrastructure. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary score of individual tourists, while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally considering instances of realistic size. Finally, we evaluate our algorithm using a real dataset extracted from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution.