利用双向全局转移模式和个人偏好识别缺失POI类别
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）服务越来越受欢迎，这为构建个性化兴趣点（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类别识别任务需要利用缺失类别之前和之后的签到信息。因此，一个长期存在的挑战是如何在移动用户的真实世界签到数据中随时有效地识别缺失的POI类别。为此，在本文中，我们提出了一种新的神经网络的方法来识别丢失的POI类别，通过整合双向全球非个人的过渡模式和用户的个人偏好。具体来说，我们精心设计了一个注意力匹配单元格，以模拟签到类别信息与非个人转换模式和个人偏好的匹配程度。最后，我们在两个真实世界的数据集上评估了我们的模型，与最先进的基线相比，这清楚地验证了它的有效性。此外，我们的模型可以自然扩展以解决下一个兴趣点类别推荐和预测任务，并具有竞争力的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
