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 Contextualized-embedding (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.

Keywords

• Social circle inference
• self-attention
• contextualized embedding
• mobility learning
• multi-label classification