Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Revisiting revisits in trajectory recommendation   Trajectory recommendation is the problem of recommending a sequence of places
in a city for a tourist to visit. It is strongly desirable for the recommended
sequence to avoid loops, as tourists typically would not wish to revisit the
same location. Given some learned model that scores sequences, how can we then
find the highest-scoring sequence that is loop-free? This paper studies this
problem, with three contributions. First, we detail three distinct approaches
to the problem -- graph-based heuristics, integer linear programming, and list
extensions of the Viterbi algorithm -- and qualitatively summarise their
strengths and weaknesses. Second, we explicate how two ostensibly different
approaches to the list Viterbi algorithm are in fact fundamentally identical.
Third, we conduct experiments on real-world trajectory recommendation datasets
to identify the tradeoffs imposed by each of the three approaches. Overall, our
results indicate that a greedy graph-based heuristic offer excellent
performance and runtime, leading us to recommend its use for removing loops at
prediction time.

10
The categories of this paper are: q-bio
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas