On the Need for Configurable Travel Recommender Systems: A Systematic Mapping Study

Travel Recommender Systems (TRSs) have been proposed to ease the burden of choice in the travel domain, by providing valuable suggestions based on user preferences. Despite the broad similarities in functionalities and data provided by TRSs, these systems are significantly influenced by the diverse and heterogeneous contexts in which they operate. This plays a crucial role in determining the accuracy and appropriateness of the travel recommendations they deliver. For instance, in contexts like smart cities and natural parks, diverse runtime information-such as traffic conditions and trail status, respectively-should be utilized to ensure the delivery of pertinent recommendations, aligned with user preferences within the specific context. However, there is a trend to build TRSs from scratch for different contexts, rather than supporting developers with configuration approaches that promote reuse, minimize errors, and accelerate time-to-market. To illustrate this gap, in this paper, we conduct a systematic mapping study to examine the extent to which existing TRSs are configurable for different contexts. The conducted analysis reveals the lack of configuration support assisting TRSs providers in developing TRSs closely tied to their operational context. Our findings shed light on uncovered challenges in the domain, thus fostering future research focused on providing new methodologies enabling providers to handle TRSs configurations.