A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we jointly study the effects of users’ social relationships, textual reviews, and POIs’ geographical proximity in order to excavate complex spatio-temporal patterns of visiting behaviors when the data quality is unreliable for location recommendation in spatio-temporal social networks. We craft a novel framework that recommends any user the POIs with effectiveness. The framework contains two significant techniques: (i) a network embedding method is adopted to learn the vectors of users and POIs in an embedding space of low dimension; (ii) a dynamic factor graph model is proposed to model various factors such as the correlation of vectors in the previous phase. A collection of experiments was carried out on two real large-scale datasets, and the experimental outcomes demonstrate the supremacy of the proposed method over the most advanced baseline algorithms owing to its highly effective and efficient performance of POI recommendation.

整个基于位置的社交网络（LBSN）中流动的大量数据为推荐兴趣点（POI）提供了支持。显着影响推荐准确性的主要挑战之一是找到动态的访问行为时空模式，由于多种因素，很难做到这一点。为了解决这个难题，我们共同研究了用户社交关系，文本评论和POI的地理位置邻近性的影响，以便在数据质量不可靠时空社交中推荐位置时挖掘复杂的时空访问行为模式网络。我们制定了一个新颖的框架，向所有用户推荐有效的POI。该框架包含两项重要技术：（i）采用网络嵌入方法，在低维的嵌入空间中学习用户和兴趣点的向量；（ii）提出了动态因素图模型来对各种因素进行建模，例如前一阶段的向量相关性。在两个真实的大规模数据集上进行了实验集合，实验结果证明了该方法相对于最先进的基线算法而言是至高无上的，这归因于其POI推荐的高效和高效性能。