With the rapid development of the mobile communication technology, mobile
trajectories of humans are massively collected by Internet service providers
(ISPs) and application service providers (ASPs). On the other hand, the rising
paradigm of knowledge graph (KG) provides us a promising solution to extract
structured "knowledge" from massive trajectory data. In this paper, we focus on
modeling users' spatio-temporal mobility patterns based on knowledge graph
techniques, and predicting users' future movement based on the "knowledge''
extracted from multiple sources in a cohesive manner. Specifically, we propose
a new type of knowledge graph, i.e., spatio-temporal urban knowledge graph
(STKG), where mobility trajectories, category information of venues, and
temporal information are jointly modeled by the facts with different relation
types in STKG. The mobility prediction problem is converted to the knowledge
graph completion problem in STKG. Further, a complex embedding model with
elaborately designed scoring functions is proposed to measure the plausibility
of facts in STKG to solve the knowledge graph completion problem, which
considers temporal dynamics of the mobility patterns and utilizes PoI
categories as the auxiliary information and background knowledge. Extensive
evaluations confirm the high accuracy of our model in predicting users'
mobility, i.e., improving the accuracy by 5.04% compared with the
state-of-the-art algorithms. In addition, PoI categories as the background
knowledge and auxiliary information are confirmed to be helpful by improving
the performance by 3.85% in terms of accuracy. Additionally, experiments show
that our proposed method is time-efficient by reducing the computational time
by over 43.12% compared with existing methods.

本文利用空间 — 时间城市知识图谱中的移动轨迹，特定地点类别信息及时间信息，设计了一个模型，旨在解决基于知识图谱技术的用户的远期移动预测问题，提高了预测精确度。同时，对 POI 类别作为背景知识和辅助信息的实验结果表明，可帮助提升预测精度。

空间 - 时间城市知识图谱启用的出行模式预测

Spatio-Temporal Urban Knowledge Graph Enabled Mobility Prediction

The accelerated growth of mobile trajectories in location-based services
brings valuable data resources to understand users' moving behaviors. Apart
from recording the trajectory data, another major characteristic of these
location-based services is that they also allow the users to connect whomever
they like. A combination of social networking and location-based services is
called as location-based social networks (LBSN). As shown in previous works,
locations that are frequently visited by socially-related persons tend to be
correlated, which indicates the close association between social connections
and trajectory behaviors of users in LBSNs. In order to better analyze and mine
LBSN data, we present a novel neural network model which can joint model both
social networks and mobile trajectories. In specific, our model consists of two
components: the construction of social networks and the generation of mobile
trajectories. We first adopt a network embedding method for the construction of
social networks: a networking representation can be derived for a user. The key
of our model lies in the component of generating mobile trajectories. We have
considered four factors that influence the generation process of mobile
trajectories, namely user visit preference, influence of friends, short-term
sequential contexts and long-term sequential contexts. To characterize the last
two contexts, we employ the RNN and GRU models to capture the sequential
relatedness in mobile trajectories at different levels, i.e., short term or
long term. Finally, the two components are tied by sharing the user network
representations. Experimental results on two important applications demonstrate
the effectiveness of our model. Especially, the improvement over baselines is
more significant when either network structure or trajectory data is sparse.

本研究提出了一种新颖的神经网络模型，通过联合建模社交网络和移动轨迹，可以更好地分析和挖掘地理位置社交网络（LBSN）数据。通过考虑四个影响移动轨迹生成过程的因素来建模移动轨迹，最终在两个重要应用的实验结果中验证了模型的有效性。