The uncontrolled growth of global tourism requires a new generation of recommendation systems that guide users toward more sustainable choices. Existing systems, however, often prioritize popularity, failing to account for the complex sustainability impacts of tourism. Addressing this gap, this paper introduces a novel, comprehensive hierarchical framework for evaluating Points of Interest based on both user-centric and sustainability criteria. We further propose a detailed, multi-faceted data acquisition and estimation strategy designed to overcome the critical challenge of data scarcity and populate this framework with robust values. This hybrid approach integrates primary data from platforms and open repositories with advanced inference methods. In particular, we propose to employ proxy variables and leverage Artificial Intelligence methods including Large Language Models to estimate hard-to-measure criteria. Building upon this approach, we present a systematic guide for implementing each criterion, specifying its relevant data source, acquisition method and evaluation scale. This work provides a foundational methodology that spans from conceptual structure to data implementation, enabling the development of next-generation recommendation systems that guide tourists toward genuinely sustainable choices.