Point-of-Interest recommendation is an increasing research and developing area within the widely adopted technologies known as Recommender Systems. Among them, those that exploit information coming from Location-Based Social Networks (LBSNs) are very popular nowadays and could work with different information sources, which pose several challenges and research questions to the community as a whole. We present a systematic review focused on the research done in the last 10 years about this topic. We discuss and categorize the algorithms and evaluation methodologies used in these works and point out the opportunities and challenges that remain open in the field. More specifically, we report the leading recommendation techniques and information sources that have been exploited more often (such as the geographical signal and deep learning approaches) while we also alert about the lack of reproducibility in the field that may hinder real performance improvements.
翻译:感兴趣的方面建议是,在广泛采用的技术(即建议系统)中,一个日益扩大的研究和发展领域,其中利用基于地点的社会网络(LBSNs)的信息,如今非常受欢迎,可以与不同的信息来源合作,这给整个社区带来了若干挑战和研究问题,我们提出一个系统审查,重点是过去10年中就这一专题进行的研究,我们讨论和分类了这些工作中使用的算法和评价方法,并指出了该领域仍然存在的机遇和挑战,更具体地说,我们报告了更经常被利用的主要建议技术和信息来源(如地理信号和深层次学习方法),同时我们也提醒注意,这一领域缺乏可阻碍实际业绩改进的再生能力。