Promoting sustainable tourism by recommending sequences of attractions with deep reinforcement learning

Developing Recommender Systems (RSs) is particularly interesting in the tourist domain, where one or more attractions have to be suggested to users based on preferences, contextual dimensions, and several other constraints. RSs usually rely on the availability of a vast amount of historical information about users’ past activities. However, this is not usually the case in the tourist domain, where acquiring complete and accurate information about the user’s behavior is complex, and providing personalized suggestions is frequently practically impossible. Moreover, even though most available Touristic RSs (T-RSs) are user-focused, the touristic domain also requires the development of systems that can promote a more sustainable form of tourism. The concept of sustainable tourism covers many aspects, from economic, social, and environmental issues to the attention to improving tourists’ experience and the needs of host communities. In this regard, one of the most important aspects is the prevention of overcrowded situations in attractions or locations (over-tourism). For this reason, this paper proposes a different kind of T-RS, which focuses more on the tourists’ impact on the destinations, trying to improve their experiences by offering better visit conditions. Moreover, instead of suggesting the next Point of Interest (PoI) to visit in a given situation, it provides a suggestion about a complete sequence of PoIs (tourist itinerary) that covers an entire day or vacation period. The proposed technique is based on the application of Deep Reinforcement Learning, where the tourist’s reward depends on the specific spatial and temporal context in which the itinerary has to be performed. The solution has been evaluated with a real-world dataset regarding the visits conducted by tourists in Verona (Italy) from 2014 to 2023 and compared with three baselines.