Trajectory-based social circle inference
Trajectory-based social circle inference
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DOI：
10.1145/3274895.3274908
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Qiang Gao;Goce Trajcevski;Fan Zhou;Kunpeng Zhang;Ting Zhong;Fengli Zhang
中科院分区：
文献类型：
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作者：
Qiang Gao;Goce Trajcevski;Fan Zhou;Kunpeng Zhang;Ting Zhong;Fengli Zhang
学习人类轨迹中的显式和隐式模式在许多基于位置的社交网络（LBSN）应用中起着重要作用，诸如轨迹分类（例如，步行、驾驶等），一个特别的问题，最近引起了很大的关注-也是我们的工作重点-是基于轨迹的社交圈推理（TSCI），旨在推断用户的社交圈（主要是社会友谊）的运动轨迹的基础上，没有任何明确的社会网络信息。由于数据稀疏性、可访问性和模型效率等方面的挑战，解决TSCI的现有方法缺乏令人满意的结果。受最近机器学习在轨迹挖掘中取得的成功的启发，在本文中，我们将TSCI定义为一个新的多标签分类问题，并开发了一个基于递归神经网络（RNN）的框架，称为DeepTSCI，使用人类移动模式来推断相应的社交圈。我们提出了三种方法来学习轨迹的潜在表示，基于：（1）双向长短期记忆（LSTM）;（2）自动编码器;（3）变分自动编码器。在真实世界数据集上进行的实验表明，我们提出的方法表现良好，与基线相比，在宏R，宏F1和准确性方面取得了显着的改善。
Learning explicit and implicit patterns in human trajectories plays an important role in many Location-Based Social Networks (LBSNs) applications, such as trajectory classification (e.g., walking, driving, etc.), trajectory-user linking, friend recommendation, etc. A particular problem that has attracted much attention recently - and is the focus of our work - is the Trajectory-based Social Circle Inference (TSCI), aiming at inferring user social circles (mainly social friendship) based on motion trajectories and without any explicit social networked information. Existing approaches addressing TSCI lack satisfactory results due to the challenges related to data sparsity, accessibility and model efficiency. Motivated by the recent success of machine learning in trajectory mining, in this paper we formulate TSCI as a novel multi-label classification problem and develop a Recurrent Neural Network (RNN)-based framework called DeepTSCI to use human mobility patterns for inferring corresponding social circles. We propose three methods to learn the latent representations of trajectories, based on: (1) bidirectional Long Short-Term Memory (LSTM); (2) Autoencoder; and (3) Variational autoencoder. Experiments conducted on real-world datasets demonstrate that our proposed methods perform well and achieve significant improvement in terms of macro-R, macro-F1 and accuracy when compared to baselines.