Tourism is an important application domain for recommender systems. In this
domain, recommender systems are for example tasked with providing personalized
recommendations for transportation, accommodation, points-of-interest (POIs),
or tourism services. Among these tasks, in particular the problem of
recommending POIs that are of likely interest to individual tourists has gained
growing attention in recent years. Providing POI recommendations to tourists
\emph{during their trip} can however be especially challenging due to the
variability of the users' context. With the rapid development of the Web and
today's multitude of online services, vast amounts of data from various sources
have become available, and these heterogeneous data sources represent a huge
potential to better address the challenges of in-trip POI recommendation
problems. In this work, we provide a comprehensive survey of published research
on POI recommendation between 2017 and 2022 from the perspective of
heterogeneous data sources. Specifically, we investigate which types of data
are used in the literature and which technical approaches and evaluation
methods are predominant. Among other aspects, we find that today's research
works often focus on a narrow range of data sources, leaving great potential
for future works that better utilize heterogeneous data sources and diverse
data types for improved in-trip recommendations.Comment: 35 pages, 19 figure