Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Off-policy evaluation for slate recommendation   This paper studies the evaluation of policies that recommend an ordered set
of items (e.g., a ranking) based on some context---a common scenario in web
search, ads, and recommendation. We build on techniques from combinatorial
bandits to introduce a new practical estimator that uses logged data to
estimate a policy's performance. A thorough empirical evaluation on real-world
data reveals that our estimator is accurate in a variety of settings, including
as a subroutine in a learning-to-rank task, where it achieves competitive
performance. We derive conditions under which our estimator is unbiased---these
conditions are weaker than prior heuristics for slate evaluation---and
experimentally demonstrate a smaller bias than parametric approaches, even when
these conditions are violated. Finally, our theory and experiments also show
exponential savings in the amount of required data compared with general
unbiased estimators.

31
The categories of this paper are: ao-sci
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
17
The categories of this paper are: q-fin
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th