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.

翻译：推荐算法的性能与其所用数据集的关键特征（如稀疏性、流行度偏差和偏好分布）紧密相关。本文通过全面的解释性分析，阐明兴趣点（POI）推荐领域中广泛数据特征的影响。为此，我们扩展了以往用于表征传统推荐问题的方法论，引入了专门针对POI推荐的新解释变量。我们将纽约市的一个POI推荐数据集按领域驱动划分为子样本，以衡量这些特征变化对多种最新POI推荐算法在准确性、新颖性和项目曝光度方面的影响。通过应用基于多元回归模型的解释框架，我们的研究发现，相关独立变量涵盖了所有类别的数据特征，可以解释算法所达到的准确性和项目曝光度的$R^2 = 85-90\%$。本研究再次确认了密度、流行度偏差和签到分布等显著数据特征在POI推荐中的关键作用。此外，我们还揭示了新因素，例如用户活动与市中心的接近程度以及用户活动的持续时间。总之，我们的工作揭示了为何某些POI推荐算法在特定推荐问题中表现出色，同时也提供了实用见解，说明应修改（或明确识别）哪些数据特征以提升性能。