Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recommendations and User Agency: The Reachability of
  Collaboratively-Filtered Information   Recommender systems often rely on models which are trained to maximize
accuracy in predicting user preferences. When the systems are deployed, these
models determine the availability of content and information to different
users. The gap between these objectives gives rise to a potential for
unintended consequences, contributing to phenomena such as filter bubbles and
polarization. In this work, we consider directly the information availability
problem through the lens of user recourse. Using ideas of reachability, we
propose a computationally efficient audit for top-$N$ linear recommender
models. Furthermore, we describe the relationship between model complexity and
the effort necessary for users to exert control over their recommendations. We
use this insight to provide a novel perspective on the user cold-start problem.
Finally, we demonstrate these concepts with an empirical investigation of a
state-of-the-art model trained on a widely used movie ratings dataset.

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The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga