Motion Based Inference of Social Circles via Self-Attention and Contextualized Embedding

Abstract

Extracting knowledge from human mobility data is an important task for many downstream applications such as point-of-interest recommendation, motion trace identification, and personalized trip planning. A specific problem that has recently spurred research interest is the so-called Social Circle Inference from Mobility data (SCIM), aiming at inferring relationships among users based on mobility data and without any explicit structured network information. The existing methods either require partial social ties or fail to model the implicit correlations between user links, thereby suffering from critical inference bias. We present a novel SCIM framework, called SCIM via self-Attention and Contextualizedembedding (SCIMAC) -a methodology capturing multiple aspects of users' check-in behavior and complex motion patterns of different users. Instead of directly applying the recurrent model on training user trajectories, the proposed method introduces a new module for context-aware check-in representation learning by adaptively incorporating the internal states of the recurrent layers, which is more effective than the context-independent check-in embedding used in existing social circle inference approaches. To model the underlying correlations between labels, SCIMAC leverages a more sophisticated label embedding technique to adjust the penalties for correlated users, enabling a better understanding of the user's hierarchy in the label space, and alleviating the inference bias. We empirically demonstrate that our SCIMAC model significantly outperforms several state-of-the-art baselines on real-world datasets.

Adversarial Mobility Learning for Human Trajectory Classification

“…Deep learning has been widely applied in various areas [6], [23]- [25]. To study human mobility, recent deep learning techniques-especially recurrent neural networks (RNNs)can well capture the long term sequential influence and mobility pattern, e.g., ST-RNN, DeepMove, DeepTSCI, and TULER [5], [7], [10], [26].…”

“…Accordingly, we adopt a recurrent neural network (RNN) to capture sequential information or long-term dependencies. In this paper, we choose two popular variants of RNN-based models, i.e., LSTM [43] and GRU [44] which are widely applied in trajectory encoding aspect [6], [8], [9], [11]. Besides, to address complex structure among check-ins, we design an attentional encoder in our AtEncoder.…”

“…These apps installed on our mobile devices, e.g., mobile phones, smartwatches, etc., can collect a large quantity of footprints left by users. These footprints provide us an opportunity to explore and study these trajectories to well understand human mobility patterns, and generate many practical and interesting applications -e.g, POI/trip recommendation [1], [2], predicting human mobility [3], [4], social circle inference [5], [6], trajectory user linking [7], [8], and etc.…”

DeepTrip

“…Deep generative models, such as Variational Auto-encoder (VAE) [10] and Generative Adversarial Networks (GANs) [8], have been widely used in computer vision and natural language processing. VAE can capture the latent variability from complex high dimensional data and have been successfully used to tackle trajectory classification problem [21] and friendship inference from human mobility [5,20]. GAN received broad attention due to the ability of generating high-quality image and fluent conversations.…”