Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation
Semi-supervised Trajectory Understanding with POI Attention for End-to-End Trip Recommendation
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DOI：
10.1145/3378890
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发表时间：
2020-02
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通讯作者：
Fan Zhou;Hantao Wu;Goce Trajcevski;A. Khokhar;Kunpeng Zhang
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作者：
Fan Zhou;Hantao Wu;Goce Trajcevski;A. Khokhar;Kunpeng Zhang
旅行规划/推荐是城市环境中大量应用的重要任务（例如，旅游、交通、社交郊游），依赖于基于位置的社交网络（LBSN）提供的服务。为了在轨迹规划中提供更大的上下文感知，LBSN联合收割机组合用户的历史轨迹以生成各种手工制作的特征，例如，游客拍摄的照片的地理标签和来自评论的文本特征。这些功能用于了解游客的偏好，然后用于生成旅行计划建议。然而，许多这样的特征是基于特定于特定数据集的先验知识或经验分析来提取的，使得相应的解决方案不能推广到不同的数据源。因此，管理移动性的一个重要问题是如何仅仅基于POI访问或用户签到来学习准确的旅游规划模型，而无需手工制作的功能工程。受最近深度学习在序列学习中取得的成功的启发，我们开发了一种基于半监督学习范式的旅游规划问题的解决方案。我们的解决方案的一个重要方面是，它不涉及任何功能工程。具体来说，我们提出了通过轨迹编码器和解码器的旅行推荐方法-一种新的端到端的方法编码的历史轨迹到向量，同时捕获的内在特征的个人兴趣点和兴趣点之间的过渡模式。我们还将历史注意力机制，在我们的序列到序列的行程推荐任务，以提高有效性。在多个公开的LBSN数据集上进行的实验表明，我们的方法具有显着的上级性能。
Trip planning/recommendation is an important task for a plethora of applications in urban settings (e.g., tourism, transportation, social outings), relying on services provided by Location-Based Social Networks (LBSN). To provide greater context-awareness in trajectory planning, LBSNs combine historical trajectories of users for generating various hand-crafted features—e.g., geo-tags of photos taken by tourists and textual characteristics derived from reviews. Those features are used to learn tourists’ preferences, which are then used to generate a travel plan recommendation. However, many such features are extracted based on prior knowledge or empirical analysis specific to particular datasets, rendering the corresponding solutions not to be generalizable to diverse data sources. Thus, one important question for managing mobility is how to learn an accurate tour planning model based solely on POI visits or user check-ins and without the efforts of hand-crafted feature engineering. Inspired by recent successes of deep learning in sequence learning, we develop a solution to the tour planning problem based on the semi-supervised learning paradigm. An important aspect of our solution is that it does not involve any feature engineering. Specifically, we propose the Trip Recommendation method via trajectory Encoder and Decoder—a novel end-to-end approach encoding historical trajectories into vectors, while capturing both the intrinsic characteristics of individual POIs and the transition patterns among POIs. We also incorporate historical attention mechanism in our sequence-to-sequence trip recommendation task to improve the effectiveness. Experiments conducted on multiple publicly available LBSN datasets demonstrate significantly superior performance of our method.