Joint learning user's activities and profiles from GPS data
Joint learning user's activities and profiles from GPS data
复制标题
从 GPS 数据中联合学习用户的活动和个人资料
DOI：
10.1145/1629890.1629894
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
Qiang Yang
中科院分区：
文献类型：
--
作者：
V. Zheng;Yu Zheng;Qiang Yang
随着支持GPS的移动的设备变得广泛可用，我们现在有机会从代表移动的用户的位置历史的大量GPS轨迹中更好地理解人类行为。在本文中，我们的目标是建立一个框架，它可以联合学习用户活动（用户在做什么）和配置文件（用户的背景是什么，如职业，性别，年龄等）。GPS数据。我们将表明，学习用户活动和学习用户配置文件可以是有益的，在本质上，所以我们试图把它们放在一起，制定一个联合学习问题下的概率协同过滤框架。特别是，对于活动识别，我们设法从原始GPS数据中提取位置语义，并将其与用户配置文件一起用作输入;我们将输出相应的日常生活活动。对于用户配置文件的学习，我们建立了一个移动的社会网络的用户之间进行的活动和已知的用户背景的相似性建模。与其他单纯从GPS数据中学习用户活动或配置文件的工作相比，我们的方法是有利的，通过利用用户活动和配置文件之间的连接进行联合学习。
As the GPS-enabled mobile devices become extensively available, we are now given a chance to better understand human behaviors from a large amount of the GPS trajectories representing the mobile users' location histories. In this paper, we aim to establish a framework, which can jointly learn the user activities (what is the user doing) and profiles (what is the user's background, such as occupation, gender, age, etc.) from the GPS data. We will show that, learning user activities and learning user profiles can be beneficial to each other in nature, so we try to put them together and formulate a joint learning problem under a probabilistic collaborative filtering framework. In particular, for activity recognition, we manage to extract the location semantics from the raw GPS data and use it, together with the user profile, as the input; and we will output the corresponding activities of daily living. For user profile learning, we build a mobile social network among the users by modeling their similarities with the performed activities and known user backgrounds. Compared with the other work on solely learning user activities or profiles from GPS data, our approach is advantageous by exploiting the connections between the user activities and profiles for joint learning.