Location based social networks, such as Foursquare and Yelp, have inspired the development of novel recommendation systems due to the massive volume and multiple types of data that their users generate on a daily basis. More recently, research studies have been focusing on utilizing structural data from these networks that relate the various entities, typically users and locations. In this work, we investigate the information contained in unique structural data of social networks, namely the lists or collections of items, and assess their potential in recommendation systems. Our hypothesis is that the information encoded in the lists can be utilized to estimate the similarities amongst POIs and, hence, these similarities can drive a personalized recommendation system or enhance the performance of an existing one. This is based on the fact that POI lists are user generated content and can be considered as collections of related POIs. Our method attempts to extract these relations and express the notion of similarity using graph theoretic, set theoretic and statistical measures. Our approach is applied on a Foursquare dataset of two popular destinations in northern Greece and is evaluated both via an offline experiment and against the opinions of local populace that we obtain via a user study. The results confirm the existence of rich similarity information within the lists and the effectiveness of our approach as a recommendation system.

基于位置的社交网络，例如Foursquare和Yelp，由于其用户每天生成的海量数据和多种类型的数据，激发了新型推荐系统的开发。最近，研究一直集中在利用来自这些网络的结构数据上，这些数据与各种实体（通常是用户和位置）相关。在这项工作中，我们调查了社交网络的独特结构数据（即列表或集合）中包含的信息项目，并评估其在推荐系统中的潜力。我们的假设是，列表中编码的信息可用于估计POI之间的相似性，因此，这些相似性可推动个性化推荐系统或增强现有推荐系统的性能。这是基于以下事实：POI列表是用户生成的内容，可以视为相关POI的集合。我们的方法尝试使用图论，设置理论和统计量来提取这些关系并表达相似性的概念。我们的方法应用于希腊北部两个热门目的地的Foursquare数据集，并通过离线实验和我们通过用户研究获得的当地居民的意见进行了评估。