Multi-modal fusion approaches for tourism: A comprehensive survey of data-sets, fusion techniques, recent architectures, and future directions

Multi-modal fusion techniques show promising results and great potential in the tourism application area. Over the past decade, various multi-modal-based fusion methods have been employed in many sub-domains of tourism, including point-of-interest (POI) recommendations, tourism demand forecasting, and travel route planning. However, the lack of architectural descriptions and consistent terminologies makes it difficult compared to other solutions. This survey provides a comprehensive analysis of current methods of multi-modal fusion and explores the diverse fusion strategies, including the early, late, hybrid, and hierarchical fusion methods. This survey provides the details of multi-modal fusion fundamentals, analyzes the benchmark dataset, and includes statistical information on these datasets. In addition, this survey discussed the particulars of federated learning-based methods and the current problems of tourism applications and suggested a solution to those problems. This survey aims to thoroughly analyze fusion-based strategies in the tourism application area to offer a valuable reference for researchers, stakeholders, and practitioners.