POI2Vec: Geographical Latent Representation for Predicting Future Visitors
POI2Vec: Geographical Latent Representation for Predicting Future Visitors
复制标题
DOI：
10.1609/aaai.v31i1.10500
复制
发表时间：
2017-02
期刊：
影响因子：
--
通讯作者：
Shanshan Feng;G. Cong;Bo An;Yeow Meng Chee
中科院分区：
文献类型：
--
作者：
Shanshan Feng;G. Cong;Bo An;Yeow Meng Chee
随着位置感知社交媒体应用的日益普及，兴趣点（POI）推荐最近得到了广泛的研究。然而，现有的研究大多从用户的角度进行探索，即为用户推荐兴趣点。相比之下，我们认为一个新的研究问题，预测用户谁将访问一个给定的POI在给定的未来时期。该问题的挑战在于难以有效地学习POI顺序转换和用户偏好，并将其整合用于预测。在这项工作中，我们提出了一个新的潜在表示模型POI2Vec，能够将地理的影响，这已被证明是非常重要的建模用户的移动行为。注意，现有的表示模型未能纳入地理影响。我们进一步提出了一种方法来联合建模用户偏好和兴趣点顺序过渡影响，以预测给定兴趣点的潜在访客。我们在2个真实世界的数据集上进行实验，以证明我们提出的方法在下一个POI预测和未来用户预测方面优于最先进的算法。
With the increasing popularity of location-aware social media applications, Point-of-Interest (POI) recommendation has recently been extensively studied. However, most of the existing studies explore from the users' perspective, namely recommending POIs for users. In contrast, we consider a new research problem of predicting users who will visit a given POI in a given future period. The challenge of the problem lies in the difficulty to effectively learn POI sequential transition and user preference, and integrate them for prediction. In this work, we propose a new latent representation model POI2Vec that is able to incorporate the geographical influence, which has been shown to be very important in modeling user mobility behavior. Note that existing representation models fail to incorporate the geographical influence. We further propose a method to jointly model the user preference and POI sequential transition influence for predicting potential visitors for a given POI. We conduct experiments on 2 real-world datasets to demonstrate the superiority of our proposed approach over the state-of-the-art algorithms for both next POI prediction and future user prediction.