  With the increasing popularity of location-based social media applications
and devices that automatically tag generated content with locations, large
repositories of collaborative geo-referenced data are appearing on-line.
Efficiently extracting user preferences from these data to determine what
information to recommend is challenging because of the sheer volume of data as
well as the frequency of updates. Traditional recommender systems focus on the
interplay between users and items, but ignore contextual parameters such as
location. In this paper we take a geospatial approach to determine locational
preferences and similarities between users. We propose to capture the
geographic context of user preferences for items using a relational graph,
through which we are able to derive many new and state-of-the-art
recommendation algorithms, including combinations of them, requiring changes
only in the definition of the edge weights. Furthermore, we discuss several
solutions for cold-start scenarios. Finally, we conduct experiments using two
real-world datasets and provide empirical evidence that many of the proposed
algorithms outperform existing location-aware recommender algorithms.
