  Point-of-Interest (POI) recommender systems provide personalized
recommendations to users and help businesses attract potential customers.
Despite their success, recent studies suggest that highly data-driven
recommendations could be impacted by data biases, resulting in unfair outcomes
for different stakeholders, mainly consumers (users) and providers (items).
Most existing fairness-related research works in recommender systems treat user
fairness and item fairness issues individually, disregarding that RS work in a
two-sided marketplace. This paper studies the interplay between (i) the
unfairness of active users, (ii) the unfairness of popular items, and (iii) the
accuracy (personalization) of recommendation as three angles of our study
triangle. We group users into advantaged and disadvantaged levels to measure
user fairness based on their activity level. For item fairness, we divide items
into short-head, mid-tail, and long-tail groups and study the exposure of these
item groups into the top-k recommendation list of users. Experimental
validation of eight different recommendation models commonly used for POI
recommendation (e.g., contextual, CF) on two publicly available POI
recommendation datasets, Gowalla and Yelp, indicate that most well-performing
models suffer seriously from the unfairness of popularity bias (provider
unfairness). Furthermore, our study shows that most recommendation models
cannot satisfy both consumer and producer fairness, indicating a trade-off
between these variables possibly due to natural biases in data. We choose the
POI recommendation as our test scenario; however, the insights should be
trivially extendable on other domains.
