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.

该研究探讨了 Point-of-Interest 推荐系统中用户和商品的公平性问题，以及推荐结果的个性化和准确性的关系，结果表明不同种类的商品存在明显的公平性偏见，而多数推荐模型在满足用户和商品公平性方面存在权衡。

关于兴趣点推荐中活跃用户和流行度偏见的不公平性问题

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

Ranking items by their probability of relevance has long been the goal of
conventional ranking systems. While this maximizes traditional criteria of
ranking performance, there is a growing understanding that it is an
oversimplification in online platforms that serve not only a diverse user
population, but also the producers of the items. In particular, ranking
algorithms are expected to be fair in how they serve all groups of users -- not
just the majority group -- and they also need to be fair in how they divide
exposure among the items. These fairness considerations can partially be met by
adding diversity to the rankings, as done in several recent works. However, we
show in this paper that user fairness, item fairness and diversity are
fundamentally different concepts. In particular, we find that algorithms that
consider only one of the three desiderata can fail to satisfy and even harm the
other two. To overcome this shortcoming, we present the first ranking algorithm
that explicitly enforces all three desiderata. The algorithm optimizes user and
item fairness as a convex optimization problem which can be solved optimally.
From its solution, a ranking policy can be derived via a novel Birkhoff-von
Neumann decomposition algorithm that optimizes diversity. Beyond the
theoretical analysis, we investigate empirically on a new benchmark dataset how
effectively the proposed ranking algorithm can control user fairness, item
fairness and diversity, as well as the trade-offs between them.

本文介绍了一种优化用户公平性、项目公平性和多样性的排名算法，并通过实验证明其在这三个方面的有效性和权衡。