  The performance of recommendation algorithms is closely tied to key
characteristics of the data sets they use, such as sparsity, popularity bias,
and preference distributions. In this paper, we conduct a comprehensive
explanatory analysis to shed light on the impact of a broad range of data
characteristics within the point-of-interest (POI) recommendation domain. To
accomplish this, we extend prior methodologies used to characterize traditional
recommendation problems by introducing new explanatory variables specifically
relevant to POI recommendation. We subdivide a POI recommendation data set on
New York City into domain-driven subsamples to measure the effect of varying
these characteristics on different state-of-the-art POI recommendation
algorithms in terms of accuracy, novelty, and item exposure. Our findings,
obtained through the application of an explanatory framework employing
multiple-regression models, reveal that the relevant independent variables
encompass all categories of data characteristics and account for as much as
$R^2 = $ 85-90\% of the accuracy and item exposure achieved by the algorithms.
Our study reaffirms the pivotal role of prominent data characteristics, such as
density, popularity bias, and the distribution of check-ins in POI
recommendation. Additionally, we unveil novel factors, such as the proximity of
user activity to the city center and the duration of user activity. In summary,
our work reveals why certain POI recommendation algorithms excel in specific
recommendation problems and, conversely, offers practical insights into which
data characteristics should be modified (or explicitly recognized) to achieve
better performance.
