The primary aim of location recommendation is to predict users’ future movement by modeling user preference. Multiple types of information have been adopted in profiling users; however, simultaneously combining them for a better recommendation is challenging. In this study, a novel location recommendation method that incorporates geographical, categorical, and social preferences with location popularity is proposed. Experimental results on two public datasets show that the proposed method significantly outperforms two state-of-the-art recommendation methods. Geographical preference generally shows more importance than both categorical and social preferences. A category hierarchy that unleashes the independent assumption of location tags improves categorical preference. Location popularity proves to be a useful metric in ranking candidate locations. The findings of this study can provide practical guidelines for location recommendation services.

位置推荐的主要目的是通过对用户偏好进行建模来预测用户的未来移动。对用户进行概要分析时采用了多种类型的信息；但是，同时组合它们以获得更好的建议是一项挑战。在这项研究中，提出了一种新颖的位置推荐方法，该方法将地理，类别和社会偏好与位置受欢迎程度相结合。在两个公共数据集上的实验结果表明，该方法明显优于两种最新的推荐方法。一般而言，地理偏好比分类偏好和社会偏好都显示出更大的重要性。释放位置标记的独立假设的类别层次结构改善了类别偏好。事实证明，位置受欢迎程度是对候选位置进行排名的有用指标。