Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Spherical Hidden Markov Model for Semantics-Rich Human Mobility
  Modeling   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.

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The categories of this paper are: cmp-lg
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: hep-th
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The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci