Points of interest (POIs) digitally represent real-world amenities as point locations. POI categories (e.g. restaurant, hotel, museum etc.) play a prominent role in several location-based applications such as social media, navigation, recommender systems, geographic information retrieval tools, and travel-related services. The majority of user queries in these applications center around POI categories. For instance, people often search for the closest pub or the best value-for-money hotel in an area. To provide valid answers to such queries, accurate and consistent information on POI categories is an essential requirement. Nevertheless, category-based annotations of POIs are often missing. The task of annotating unlabeled POIs in terms of their categories — known as POI classification — is commonly achieved by means of machine learning (ML) models, often referred to as classifiers. Central to this task is the extraction of known features from pre-labeled POIs in order to train the classifiers and, then, use the trained models to categorize unlabeled POIs. However, the set of features used in this process can heavily influence the classification results. Research on defining the influence of different features on the categorization of POIs is currently lacking. This paper contributes a study of feature importance for the classification of unlabeled POIs into categories. We define five feature sets that address operation based, review-based, topic-based, neighborhood-based, and visual attributes of POIs. Contrary to existing studies that predominantly use multi-class classification approaches, and in order to assess and rank the influence of POI features on the categorization task, we propose both a multi-class and a binary classification approach. These, respectively, predict the place category among a specified set of POI categories, or indicate whether a POI belongs to a certain category. Using POI data from Amsterdam and Athens to implement and evaluate our study approach, we show that operation based features, such as opening or visiting hours throughout the day, are the most important place category predictors. Moreover, we demonstrate that the use of feature combinations, as opposed to the use of individual features, improves the classification performance by an average of 15%, in terms of F1-score.

兴趣点（POI）以数字方式将现实生活中的便利设施表示为地点。POI类别（例如，餐厅，酒店，博物馆等）在几种基于位置的应用程序（例如社交媒体，导航，推荐系统，地理信息检索工具以及与旅行相关的服务）中扮演着重要角色。这些应用程序中的大多数用户查询都围绕POI类别进行。例如，人们经常搜索最近的酒吧或最物有所值的酒店在一个区域中。为了提供对此类查询的有效答案，关于POI类别的准确且一致的信息是必不可少的要求。但是，基于类别的POI注释通常会丢失。根据类别对未标记的POI进行注释的任务（称为POI分类）通常通过机器学习（ML）模型（通常称为分类器）来实现。这项任务的核心是从预先标记的POI中提取已知特征，以训练分类器，然后使用经过训练的模型对未标记的POI进行分类。但是，此过程中使用的一组功能会严重影响分类结果。当前缺乏定义不同特征对POI分类的影响的研究。本文有助于对将未标记POI进行分类的特征重要性进行研究。我们定义了五个功能集，以解决基于兴趣点的基于操作，基于审阅，基于主题，基于邻域和视觉属性的问题。与主要使用多类分类方法的现有研究相反，为了评估和排名POI功能对分类任务的影响，我们提出了多类和二元分类方法。这些分别预测一组指定的POI类别中的地点类别，或指示POI是否属于某个类别。利用阿姆斯特丹和雅典的POI数据来实施和评估我们的学习方法，我们证明了基于运营的功能，例如全天的开放时间或参观时间，是最重要的地点类别预测指标。此外，我们证明，相对于使用单个功能，使用功能组合可以将分类性能平均提高F1分数15％。