Human mobility annotation aims to assign mobility records the corresponding visiting Point-of-Interests (POIs). It is one of the most fundamental problems for understanding human mobile behaviors. In literature, many efforts have been devoted to annotating mobility records in a pointwise or trajectory-wise manner. However, the user preference factor is not fully explored and, worse still, the mobile peer influence factor has never been integrated. To this end, in this article, we propose a novel framework, named JEPPI, to jointly exploit user preference and mobile peer influence to tackle the problem. In our JEPPI, we first unify the two distinct factors in a behavior-driven user-POI graph. This graph enables us to model user preference with user-POI visiting relationships, and model two types of mobile peer influence with co-location and co-visiting peer relationships, respectively. Moreover, we devise an equivalence-emphasizing metric to reduce redundancy in the second-order co-visiting peer influence. In addition, a mutual augmentation learning approach is proposed to preserve the latent structures of various factors exploited. Notably, our learning approach preserves all factors in a shared representation space such that user preference is learned with mobile peer influence being considered at the same time, and vice versa. In this way, the different factors are mutually augmented and semantically integrated to enhance human mobility annotation. Finally, using two large-scale real-world datasets, we conduct extensive experiments to demonstrate the superiority of our approach compared with the state-of-the-art annotation methods.

中文翻译：

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利用用户偏好和移动同行影响进行人类移动注释

人类流动性注释旨在为流动性记录分配相应的访问兴趣点 (POI)。它是理解人类移动行为的最基本问题之一。在文献中，许多努力都致力于以逐点或逐轨迹的方式注释移动记录。然而，用户偏好因素并未得到充分挖掘，更糟糕的是，移动端影响因素从未被整合。为此，在本文中，我们提出了一个名为 JEPPI 的新框架，以共同利用用户偏好和移动对等方影响来解决问题。在我们的 JEPPI 中，我们首先在行为驱动的用户兴趣点图中统一了两个不同的因素。该图使我们能够使用用户-POI 访问关系对用户偏好进行建模，并分别对两种类型的移动对等影响与同地和共同访问对等关系进行建模。此外，我们设计了一个强调等价的指标，以减少二阶共同访问同行影响的冗余。此外，提出了一种相互增强学习方法来保留所利用的各种因素的潜在结构。值得注意的是，我们的学习方法将所有因素保留在共享表示空间中，以便在学习用户偏好的同时考虑移动对等影响，反之亦然。通过这种方式，不同的因素相互增强并在语义上整合以增强人类移动性注释。最后，使用两个大规模的真实世界数据集，