DualSIN: Dual Sequential Interaction Network for Human Intentional Mobility Prediction
DualSIN: Dual Sequential Interaction Network for Human Intentional Mobility Prediction
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DOI：
10.1145/3397536.3422221
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Quanjun Chen;Renhe Jiang;Chuang Yang;Z. Cai;Z. Fan;K. Tsubouchi;R. Shibasaki;Xuan Song
中科院分区：
文献类型：
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作者：
Quanjun Chen;Renhe Jiang;Chuang Yang;Z. Cai;Z. Fan;K. Tsubouchi;R. Shibasaki;Xuan Song
如今，GPS设备呈爆炸性增长，产生了与人们外出相关的海量轨迹数据。通过这些海量的位置数据，许多研究旨在分析城市发展中的人员流动性，如人员流动预测/建模、POI(Point Of Interest)推荐等。然而，轨迹数据只包含时间戳和位置信息。人类运动的意图并不明确，因此很难理解人们为什么要去哪里。活动前的意图可能对分析和预测人类流动性具有重要意义，这一点直到现在才被现有的研究考虑在内。因此，在本研究中，我们提出了一个全新的概念--人的意向流动，旨在利用意向信息来预测人们的外向行为。我们仔细地利用用户的搜索请求来感知他的意图以及强度。例如，如果用户在短时间内多次搜索某个POI，则表示前往该POI的意愿相对较高。然后，为了充分利用这种意图表示来预测用户是否会访问搜索到的POI，我们特别设计了双序列交互网络(DualSIN)作为一种新颖而独特的深度学习模型，它能够有效地捕捉两种序列信息(即搜索序列和移动序列)与典型类别信息(即用户属性)之间的复杂交互作用。最后，我们在从雅虎收集的真实数据集上对我们的模型进行了评估。日本门户应用，并证明它可以在多个POI搜索查询上取得比最先进模型更令人满意的性能。
Nowadays, GPS devices have increased explosively and produced huge amounts of trajectory data related to people's outgoing. Through those big location data, many researches aim to analyze human mobility for urban development, such as human movement prediction/modeling, POI (Point-Of-Interest) recommendation. However, trajectory data only contains timestamp and location information. The intention of human movement is not explicit so that it is hard to understand why people go to somewhere. The intention prior to the activity could be of great significance for analyzing and predicting human mobility, which has not been taken into consideration by the existing researches until the present. Thus, in this study, we propose a brand-new concept called human intentional mobility, aiming to employ intention information to predict people's outgoing. We carefully utilize user's search query to sense his intention as well as the intensity. For instance, if a user searches a certain POI for many times in a short period, it will represent a relatively high intention to go there. Then, to fully utilize this intention representation for predicting whether user will visit searched POI or not, we specially design Dual Sequential Interaction Network (DualSIN) as a novel and unique deep-learning model, which can effectively capture the sophisticated interactions among two kinds of sequential information (i.e., search sequence and mobility sequence) and typical categorical information (i.e., user attributes). Last, we evaluate our model on real-world dataset collected from Yahoo! Japan portal application, and demonstrate that it can achieve superior satisfactory performances to the-state-of-the-art models on multiple POI search queries.