A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling

TL;DR

This paper introduces SHMM, a novel spherical hidden Markov model that effectively captures semantics-rich human mobility patterns by integrating location, time, and text data using vMF distributions, improving prediction accuracy.

Contribution

The paper proposes SHMM, a multi-modal model that leverages vMF distributions for text embeddings, with theoretical guarantees for parameter inference and superior performance over existing models.

Findings

SHMM outperforms state-of-the-art models in next location prediction.

Theoretical proof of EM algorithm applicability to vMF distributions.

SHMM achieves lower training costs while modeling semantics-rich mobility.

Abstract

We study the problem of modeling human mobility from semantic trace data, wherein each GPS record in a trace is associated with a text message that describes the user's activity. Existing methods fall short in unveiling human movement regularities, because they either do not model the text data at all or suffer from text sparsity severely. We propose SHMM, a multi-modal spherical hidden Markov model for semantics-rich human mobility modeling. Under the hidden Markov assumption, SHMM models the generation process of a given trace by jointly considering the observed location, time, and text at each step of the trace. The distinguishing characteristic of SHMM is the text modeling part. We use fixed-size vector representations to encode the semantics of the text messages, and model the generation of the l2-normalized text embeddings on a unit sphere with the von Mises-Fisher (vMF) distribution. Compared with other alternatives like multi-variate Gaussian, our choice of the vMF distribution not only incurs much fewer parameters, but also better leverages the discriminative power of text embeddings in a directional metric space. The parameter inference for the vMF distribution is non-trivial since it involves functional inversion of ratios of Bessel functions. We theoretically prove that: 1) the classical Expectation-Maximization algorithm can work with vMF distributions; and 2) while closed-form solutions are hard to be obtained for the M-step, Newton's method is guaranteed to converge to the optimal solution with quadratic convergence rate. We have performed extensive experiments on both synthetic and real-life data. The results on synthetic data verify our theoretical analysis; while the results on real-life data demonstrate that SHMM learns meaningful semantics-rich mobility models, outperforms state-of-the-art mobility models for next location prediction, and incurs lower training cost.