Pacific Graphics是亚洲图形协会的旗舰会议。作为一个非常成功的会议系列,太平洋图形公司为太平洋沿岸以及世界各地的研究人员,开发人员,从业人员提供了一个高级论坛,以介绍和讨论计算机图形学及相关领域的新问题,解决方案和技术。太平洋图形会议的目的是召集来自各个领域的研究人员,以展示他们的最新成果,开展合作并为研究领域的发展做出贡献。会议将包括定期的论文讨论会,进行中的讨论会,教程以及由与计算机图形学和交互系统相关的所有领域的国际知名演讲者的演讲。 官网地址:


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