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Recommendation systems are a fundamental tool for hotels to adopt a differentiating competitive strategy. The main purpose of this work is to use machine learning techniques to treat imbalanced data sets, not applied until now in the tourism field. These techniques have allowed the authors to analyse the influence of imbalance data on hotel recommendation models and how this phenomenon affects client dissatisfaction.

An opinion survey was conducted among hotel customers of different categories in 120 different countries. A total of 135.102 surveys were collected over eleven quarters. A longitudinal design was conducted during this period. A binary logistic model was applied using the function generalized lineal model (GLM).

Through the analysis of a representative amount of data, the authors empirically demonstrate that the imbalance phenomenon is systematically present in hotel recommendation surveys. In addition, the authors show that the imbalance exists independently of the period in which the survey is done, which means that it is intrinsic to recommendation surveys on this topic. The authors demonstrate the improvement of recommendation systems highlighting the presence of imbalance data and consequences for marketing strategies.

The main contribution of the current work is to apply to the tourism sector the framework for imbalanced data, typically used in the machine learning, improving predictive models.

This paper aims to investigate the competitive potential of different classes of municipalities within larger metropolitan areas, considering three dimensions of place competitiveness, associated to contemporary economic recovery agendas: the “dual transition” (green and digital) and socio-economic resilience.

The proposed methodology is divided in two stages, the first aiming at developing a new Index of Urban Competitiveness, based on three key dimensions of place development, by using principal component analysis and hierarchical cluster analysis; the second intends to identify municipalities’ main competitive assets, throughout the examination of the existing links between the overall competitiveness index and intra-metropolitan place conditions in each dimension. This methodology is applied to the metropolitan areas of Porto and Lisbon.

The analysis shows a weak link between population size and urban competitiveness, suggesting that economic recovery investments primarily targeting larger municipalities will not necessarily lead to greater metropolitan competitive advantages. On the contrary, taking into consideration place-based interventions for different “clubs” of municipalities would more likely contribute to enhance competitive performance and valorise territorial assets. Furthermore, while the relationship between competitiveness and environmental performance appears to be non-linear, digitalization and economic and social resilience prove to be key for urban competitive potential.

By drawing on contemporary notions of urban competitiveness, the work proposes a revised method to evaluate competitiveness, latent qualities and intrinsic features of places, constituting an initial step to conceive suitable metropolitan development and investment strategies for economic recovery.