论文标题
用于移动用户分析的增强模仿图表学习
Reinforced Imitative Graph Learning for Mobile User Profiling
论文作者
论文摘要
移动用户分析是指从移动活动中提取用户特征的努力。为了捕获用户特性的动态变化以生成有效的用户分析,我们提出了一个基于模仿的移动用户分析框架。考虑到教授自主代理以基于用户配置文件模仿用户移动性的目的,当代理可以完美地模仿用户行为模式时,用户配置文件是最准确的。分析框架被制定为强化学习任务,在该任务中,代理是下一个访问的计划者,操作是用户将下一步访问的POI,环境状态是用户和空间实体的融合表示。用户访问POI的事件将构建一个新状态,这可以帮助代理商更准确地预测用户的移动性。在框架中,我们引入了一个空间知识图(kg),以表征有关连接空间实体的用户访问的语义。此外,我们制定了一种相互效力的策略来量化随着时间的流逝而发展的状态。沿着这些线路,我们为移动用户分析开发了强化模仿图形学习框架。最后,我们进行了广泛的实验,以证明我们的方法的优势。
Mobile user profiling refers to the efforts of extracting users' characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user's profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users' mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.