Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LightMove: A Lightweight Next-POI Recommendation for Taxicab Rooftop
  Advertising   Mobile digital billboards are an effective way to augment brand-awareness.
Among various such mobile billboards, taxicab rooftop devices are emerging in
the market as a brand new media. Motov is a leading company in South Korea in
the taxicab rooftop advertising market. In this work, we present a lightweight
yet accurate deep learning-based method to predict taxicabs' next locations to
better prepare for targeted advertising based on demographic information of
locations. Considering the fact that next POI recommendation datasets are
frequently sparse, we design our presented model based on neural ordinary
differential equations (NODEs), which are known to be robust to
sparse/incorrect input, with several enhancements. Our model, which we call
LightMove, has a larger prediction accuracy, a smaller number of parameters,
and/or a smaller training/inference time, when evaluating with various
datasets, in comparison with state-of-the-art models.

17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga