Predicting the next place to visit is a key in human mobility behavior modeling, which plays a significant role in various fields, such as epidemic control, urban planning, traffic management, and travel recommendation. To achieve this, one typical solution is designing modules based on RNN to capture their preferences to various locations. Although these RNN-based methods can effectively learn individual's hidden personalized preferences to her visited places, the interactions among users can only be weakly learned through the representations of locations. Targeting this, we propose an end-to-end framework named personalized and group preference guided network (PG$^2$Net), considering the users' preferences to various places at both individual and collective levels. Specifically, PG$^2$Net concatenates Bi-LSTM and attention mechanism to capture each user's long-term mobility tendency. To learn population's group preferences, we utilize spatial and temporal information of the visitations to construct a spatio-temporal dependency module. We adopt a graph embedding method to map users' trajectory into a hidden space, capturing their sequential relation. In addition, we devise an auxiliary loss to learn the vectorial representation of her next location. Experiment results on two Foursquare check-in datasets and one mobile phone dataset indicate the advantages of our model compared to the state-of-the-art baselines. Source codes are available at https://github.com/urbanmobility/PG2Net.
翻译:预测下一个访问地点是人类流动行为模型中的关键,该框架在诸如流行病控制、城市规划、交通管理和旅行建议等各个领域起着重要作用。为此,一个典型的解决办法是设计基于 RNN 的模块,以捕捉对不同地点的偏好。虽然这些基于RNN 的方法可以有效地了解个人对所访问地点的隐藏个人偏好,但用户之间的互动只能通过地点的表示方式来微弱地了解。为此,我们提议了一个名为个性化和群体偏好指导网络的端到端框架(PG$2, Net),考虑到用户对个人和集体两级不同地点的偏好。具体地说,PG$2$Net将Bi-LSTM和关注机制组合成一个模块,以捕捉到每个用户的长期流动趋势。为了了解人群的偏好,我们只能利用访问的时空信息来构建一个空间-时间模型。我们采用图表嵌入方法将用户的轨迹映入一个隐藏的空间,捕捉到他们的相邻关系。此外,我们还设计了一种辅助性损失,以学习其下一个位置的矢量/矢量基数据基数据模型。