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Tourism is an important application domain for recommender systems. In thisdomain, recommender systems are for example tasked with providing personalizedrecommendations for transportation, accommodation, points-of-interest (POIs),or tourism services. Among these tasks, in particular the problem ofrecommending POIs that are of likely interest to individual tourists has gainedgrowing attention in recent years. Providing POI recommendations to tourists\emph{during their trip} can however be especially challenging due to thevariability of the users' context. With the rapid development of the Web andtoday's multitude of online services, vast amounts of data from various sourceshave become available, and these heterogeneous data sources represent a hugepotential to better address the challenges of in-trip POI recommendationproblems. In this work, we provide a comprehensive survey of published researchon POI recommendation between 2017 and 2022 from the perspective ofheterogeneous data sources. Specifically, we investigate which types of dataare used in the literature and which technical approaches and evaluationmethods are predominant. Among other aspects, we find that today's researchworks often focus on a narrow range of data sources, leaving great potentialfor future works that better utilize heterogeneous data sources and diversedata types for improved in-trip recommendations.

A Survey on Point-of-Interest Recommendations Leveraging Heterogeneous Data