标题： 利用双向全局转移模式和个人偏好进行缺失POI类别识别 显示英文标题

标题： Exploiting Bi-directional Global Transition Patterns and Personal Preferences for Missing POI Category Identification

摘要： 近年来，基于位置的社交网络（LBSN）服务日益流行，这为构建个性化的兴趣点（POI）推荐系统提供了前所未有的机会。现有的POI推荐和位置预测任务从单一方向的角度利用过去的信息进行未来的推荐或预测，而缺失POI类别识别任务需要同时利用缺失类别的前后签到信息。因此，一个长期存在的挑战是如何在移动用户的实际签到数据中有效识别任何时间的缺失POI类别。为此，本文提出了一种新的神经网络方法，通过整合双向全局非个性化转移模式和用户的个性化偏好来识别缺失的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非个性化转移模式和个性化偏好的匹配程度。最后，我们在两个真实数据集上评估了我们的模型，结果明确验证了其有效性，相比最先进的基线方法具有优势。此外，我们的模型可以自然扩展以解决下一个POI类别推荐和预测任务，并表现出具有竞争力的性能。 显示英文摘要

摘要： 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.