Recent years have witnessed the increasing popularity of Location-based Social Network (LBSN) services, which provides unparalleled opportunities to build personalized Point-of-Interest (POI) recommender systems. Existing POI recommendation and location prediction tasks utilize past information for future recommendation or prediction from a single direction perspective, while the missing POI category identification task needs to utilize the check-in information both before and after the missing category. Therefore, a long-standing challenge is how to effectively identify the missing POI categories at any time in the real-world check-in data of mobile users. To this end, in this paper, we propose a novel neural network approach to identify the missing POI categories by integrating both bi-directional global non-personal transition patterns and personal preferences of users. Specifically, we delicately design an attention matching cell to model how well the check-in category information matches their non-personal transition patterns and personal preferences. Finally, we evaluate our model on two real-world datasets, which clearly validate its effectiveness compared with the state-of-the-art baselines. Furthermore, our model can be naturally extended to address next POI category recommendation and prediction tasks with competitive performance.

近年来，目睹了基于位置的社交网络（LBSN）服务的日益普及，该服务为构建个性化的兴趣点（POI）推荐系统提供了无与伦比的机会。现有的POI推荐和位置预测任务从单个方向的角度利用过去的信息进行将来的推荐或预测，而丢失的POI类别标识任务需要在丢失的类别之前和之后都使用签到信息。因此，一个长期的挑战是如何在移动用户的真实值机数据中随时有效地识别丢失的POI类别。为此，在本文中，我们提出了一种新颖的神经网络方法，通过整合双向全局非个人过渡模式和用户的个人偏好来识别缺失的POI类别。具体来说，我们精心设计了一个注意力匹配单元，以建模签到类别信息与他们的非个人过渡模式和个人偏好的匹配程度。最后，我们在两个真实的数据集上评估了我们的模型，与最新的基线相比，这些数据清楚地验证了其有效性。此外，我们的模型可以自然扩展以解决具有竞争力的下一个POI类别推荐和预测任务。我们精心设计了一个注意力匹配单元，以模拟签到类别信息与他们的非个人过渡模式和个人偏好的匹配程度。最后，我们在两个真实的数据集上评估了我们的模型，与最新的基线相比，这些数据清楚地验证了其有效性。此外，我们的模型可以自然扩展以解决具有竞争力的下一个POI类别推荐和预测任务。我们精心设计了一个注意力匹配单元，以模拟签到类别信息与他们的非个人过渡模式和个人偏好的匹配程度。最后，我们在两个真实的数据集上评估了我们的模型，与最新的基线相比，这些数据清楚地验证了其有效性。此外，我们的模型可以自然扩展以解决具有竞争力的下一个POI类别推荐和预测任务。